content_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm\n",
    "import seaborn as sns\n",
    "import spacy\n",
    "import scipy\n",
    "from tqdm.auto import tqdm\n",
    "np.float = float    \n",
    "\n",
    "import sklearn\n",
    "import sklearn.feature_extraction.text\n",
    "import sklearn.pipeline\n",
    "import sklearn.preprocessing\n",
    "import sklearn.datasets\n",
    "import sklearn.cluster\n",
    "import sklearn.decomposition\n",
    "import sklearn.metrics\n",
    "import gensim\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "\n",
    "from pyanno.measures import pairwise_matrix, agreement, cohens_kappa, cohens_weighted_kappa, fleiss_kappa, krippendorffs_alpha, pearsons_rho, scotts_pi, spearmans_rho\n",
    "from pyanno.annotations import AnnotationsContainer\n",
    "from pyanno.models import ModelA, ModelBt, ModelB\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "import openai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_preprocess_df(file_path, video_date, video_title):\n",
    "    ''' \n",
    "    Load scrpaed comments csv into DataFrame, preprocess column information\n",
    "    Arg:\n",
    "        file_path (str): input csv file path of all comments and replies in a given video\n",
    "        video_date (str): the publish date of this news video, different from comment's publish date\n",
    "    '''\n",
    "\n",
    "    df = pd.read_csv(file_path)\n",
    "    df.rename(columns={'PublishedAt': 'Comment PublishedAt'}, inplace=True)\n",
    "    df['Comment PublishedAt'] = pd.to_datetime(df['Comment PublishedAt']) # ensure entries are DataTime\n",
    "    df['Comment PublishedAt'] = df['Comment PublishedAt'].dt.strftime('%Y-%m-%d') # ignore hour/minute/second\n",
    "    df['Video PublishedAt'] = pd.Timestamp(video_date)\n",
    "    df['Video Title'] = video_title\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load 2020 comment data\n",
    "fox3_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2003_comments.csv', '2020-03-09', 'Tucker: We must remain calm but not complacent about the coronavirus')\n",
    "fox4_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2004_comments.csv', '2020-04-10', '10 percent of US workforce now unemployed')\n",
    "fox5_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2005_comments.csv', '2020-05-17', 'Gutfeld: Where were the media warnings back when coronavirus pandemic started?')\n",
    "fox6_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2006_comments.csv', '2020-06-26', \"Coronavirus cases are surging; Who's to blame?\")\n",
    "fox7_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2007_comments.csv', '2020-07-27', \"Dr. Fauci says he's 'cautiously optimistic' regarding coronavirus vaccine\")\n",
    "fox8_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2008_comments.csv', '2020-08-11', 'Big 10 conference officially postpones 2020 football season')\n",
    "fox9_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2009_comments.csv', '2020-09-15', \"Coronavirus whistleblower speaks out about possible COVID origin on 'Tucker'\")\n",
    "fox10_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2010_comments.csv', '2020-10-11', \"Trump says he no longer has coronavirus and is 'immune'\")\n",
    "fox11_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2011_comments.csv', '2020-11-09', 'Biden could propose national mask mandate as part of COVID-19 plan')\n",
    "fox12_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2020/fox2012_comments.csv', '2020-12-27', \"Judge Jeanine: Congress's incompetence over COVID relief bill\")\n",
    "\n",
    "abc1_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2001_comments.csv', '2020-01-27', 'Race to contain deadly coronavirus with 5 confirmed US cases')\n",
    "abc2_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2002_comments.csv', '2020-02-24', 'Italy struggles to contain new coronavirus outbreak')\n",
    "abc3_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2003_comments.csv', '2020-03-24', 'Lesson from South Korea on how to slow the COVID-19 spread')\n",
    "abc4_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2004_comments.csv', '2020-04-06', 'Queen Elizabeth address public amid coronavirus outbreak')\n",
    "abc5_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2005_comments.csv', '2020-05-27', 'Facing cancer during COVID-19')\n",
    "abc6_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2006_comments.csv', '2020-06-25', 'Latest updates on COVID-19: June 25, 2020')\n",
    "abc7_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2007_comments.csv', '2020-07-02', 'US coronavirus infections surge amid new warnings, closures | WNT')\n",
    "abc8_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2008_comments.csv', '2020-08-24', \"The new COVID-19 travel trend is a 'flexcation'\")\n",
    "abc9_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2009_comments.csv', '2020-09-22', \"COVID-19 crisis threatens children's mental health\")\n",
    "abc10_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2010_comments.csv', '2020-10-15', 'Joe Biden pressed whether he would take a COVID-19 vaccine l ABC News Town Hall')\n",
    "abc11_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2011_comments.csv', '2020-11-25', 'Thanksgiving COVID-19 warning')\n",
    "abc12_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2020/abc2012_comments.csv', '2020-12-07', \"Sean Penn's COVID-19 testing mission\")\n",
    "\n",
    "msnbc2_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2002_comments.csv', '2020-02-08', '13 Americans Infected With COVID-19 On Cruise Ship In Japan')\n",
    "msnbc3_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2003_comments.csv', '2020-03-16', 'Historic: First Person to Test Covid Vaccine talks to Ari Melber in 2020')\n",
    "msnbc4_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2004_comments.csv', '2020-03-31', \"See Bill Gates' Pandemic Warnings Before Covid: Ari Melber Breakdown\")\n",
    "msnbc5_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2005_comments.csv', '2020-05-25', 'Global Carbon Emissions Drop By 17% Amid COVID-19 Pandemic | MTP Daily')\n",
    "msnbc6_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2006_comments.csv', '2020-06-26', \"Gov. Abbott's Steps 'Fall Short' Of What Texas Needs Amid Rising COVID-19 Cases\")\n",
    "msnbc7_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2007_comments.csv', '2020-07-08', 'Pathologist Shares Findings From Autopsies Of COVID-19 Patients | MTP Daily')\n",
    "msnbc8_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2008_comments.csv', '2020-08-27', \"Joy Reid On Trump Downplaying COVID-19: There's No Politician I'd Die For\")\n",
    "msnbc9_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2009_comments.csv', '2020-09-02', \"Fauci: U.S. Must Shrink Daily COVID Case Baseline To '10,000 Or Less' | Andrea Mitchell\")\n",
    "msnbc10_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2010_comments.csv', '2020-10-06', 'Trump Returns To WH, Says To Not Be Afraid Of Coronavirus | Morning Joe')\n",
    "msnbc11_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2011_comments.csv', '2020-11-29', \"Doctor on COVID: 'Going Back To Normal Will Prolong This War'\")\n",
    "msnbc12_df = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2020/msnbc2012_comments.csv', '2020-12-18', \"FDA Grants Emergency Authorization To Moderna's Covid-19 Vaccine | All In\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load 2021 comment data\n",
    "fox1_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2101_comments.csv', '2021-01-30', 'Sen Cassidy lays out what it will take for GOP to support COVID-19 relief')\n",
    "fox2_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2102_comments.csv', '2021-02-06', \"COVID-19 vaccinations shouldn't be a prerequisite to reopening schools: infectious diseases expert\")\n",
    "fox3_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2103_comments.csv', '2021-03-05', 'Top Cuomo aides altered COVID-19 nursing death tolls: Report')\n",
    "fox4_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2104_comments.csv', '2021-04-28', 'Biden, Harris called out by governors for skipping COVID calls')\n",
    "fox5_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2105_comments.csv', '2021-05-31', 'Biden calls for intel report on COVID origins')\n",
    "fox6_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2106_comments.csv', '2021-06-10', 'Biden to give 500M COVID vaccine doses to low-income countries')\n",
    "fox7_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2107_comments.csv', '2021-07-14', \"Californians 'fed up' with COVID restrictions: 'Enough is enough'\")\n",
    "fox8_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2108_comments.csv', '2021-08-01', \"NIH director: Delta variant is a 'different virus,' could hit reset button\")\n",
    "fox9_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2109_comments.csv', '2021-09-09', \"Gutfeld shreds Biden for 'ginning up anger' during COVID speech\")\n",
    "fox10_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2110_comments.csv', '2021-10-18', \"'The Five' blast COVID hypocrisy from the elites\")\n",
    "fox11_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2111_comments.csv', '2021-11-29', \"McEnany: No one has politicized the virus more than Biden\")\n",
    "fox12_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/fox_2021/fox2112_comments.csv', '2021-12-01', \"'The Five' rip new Biden COVID restrictions\")\n",
    "\n",
    "abc1_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2101_comments.csv', '2021-01-21', \"Coronavirus crisis\")\n",
    "abc2_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2102_comments.csv', '2021-02-22', \"Remembering the lives lost to COVID-19\")\n",
    "abc3_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2103_comments.csv', '2021-03-24', \"Some COVID-19 patients experiencing eye problems\")\n",
    "abc4_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2104_comments.csv', '2021-04-26', \"India experiencing 'COVID tsunami'\")\n",
    "abc5_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2105_comments.csv', '2021-05-10', \"India's healthcare system pushed to its limits as coronavirus crisis deepens\")\n",
    "abc6_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2106_comments.csv', '2021-06-23', \"Biden expected to miss July 4th COVID vaccine goal\")\n",
    "abc7_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2107_comments.csv', '2021-07-20', \"‘Breakthrough’ COVID cases hit the White House\")\n",
    "abc8_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2108_comments.csv', '2021-08-25', \"Data supports booster shots increasing COVID-19 protection\")\n",
    "abc9_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2109_comments.csv', '2021-09-27', \"Biden receives COVID Pfizer booster shot at White House\")\n",
    "abc10_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2110_comments.csv', '2021-10-21', \"ABC News journalist makes emotional journey home to Australia\")\n",
    "abc11_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2111_comments.csv', '2021-11-10', \"Pfizer asks FDA to approve COVID-19 boosters for ages 18 and up\")\n",
    "abc12_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/abc_2021/abc2112_comments.csv', '2021-12-30', \"US shatters daily COVID-19 case record\")\n",
    "\n",
    "msnbc1_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2101_comments.csv', '2021-01-10', \"U.S. Could Hit 1 Million COVID-19 Deaths By May 1st, Expert Believes\")\n",
    "msnbc2_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2102_comments.csv', '2021-02-19', \"Doctors Call For Expedited Covid Vaccination Before Variants Become Widespread In U.S. | Katy Tur\")\n",
    "msnbc3_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2103_comments.csv', '2021-03-12', \"One Year with Covid: Celebrating The Lives Of Those We Lost And Families Left Behind\")\n",
    "msnbc4_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2104_comments.csv', '2021-04-14', \"Everything You Need To Know (And More) About Johnson & Johnson Covid Vaccine Pause | Rachel Maddow\")\n",
    "msnbc5_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2105_comments.csv', '2021-05-12', \"Dr. Fauci Explains Emergency Use Authorization For Covid Vaccines\")\n",
    "msnbc6_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2106_comments.csv', '2021-06-09', \"Dr. Anthony Fauci On Covid Origins, New Variant\")\n",
    "msnbc7_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2107_comments.csv', '2021-07-11', \"There Are 'More Consequences To Covid-19 Than Just Death'\")\n",
    "msnbc8_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2108_comments.csv', '2021-08-11', \"How A 'Mini-Trump' Is Running Florida Into The Ground Amidst Covid-19 Deaths\")\n",
    "msnbc9_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2109_comments.csv', '2021-09-30', \"Unvaccinated Covid Patients Still Overwhelming Health Care Workers\")\n",
    "msnbc10_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2110_comments.csv', '2021-10-01', \"California To Require Covid Vaccines For Schoolchildren\")\n",
    "msnbc11_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2111_comments.csv', '2021-11-12', \"For Facts Sake: Nearly A Million Children Vaccinated For Covid-19\")\n",
    "msnbc12_df1 = load_preprocess_df('/Users/itsyuzhou/Winter2024/MA_thesis/youtube_comments/comments/msnbc_2021/msnbc2112_comments.csv', '2021-12-28', \"Puerto Rico Covid Cases Soar Nearly 2800% In Two Weeks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Comment PublishedAt</th>\n",
       "      <th>Comment</th>\n",
       "      <th>Video PublishedAt</th>\n",
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       "      <th>0</th>\n",
       "      <td>2022-06-15</td>\n",
       "      <td>These people are sick trash anyone thinking of...</td>\n",
       "      <td>2021-11-12</td>\n",
       "      <td>For Facts Sake: Nearly A Million Children Vacc...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-03</td>\n",
       "      <td>More children drown EVERY year than die from C...</td>\n",
       "      <td>2021-11-12</td>\n",
       "      <td>For Facts Sake: Nearly A Million Children Vacc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-10-17</td>\n",
       "      <td>That’s Rican’s for ya 😷😷😷😷😷😷😷😷</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-12</td>\n",
       "      <td>Remember vaccines are safe and effective. safe...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
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      "text/plain": [
       "  Comment PublishedAt                                            Comment  \\\n",
       "0          2022-06-15  These people are sick trash anyone thinking of...   \n",
       "1          2022-02-03  More children drown EVERY year than die from C...   \n",
       "2          2022-10-17                     That’s Rican’s for ya 😷😷😷😷😷😷😷😷   \n",
       "3          2022-02-12  Remember vaccines are safe and effective. safe...   \n",
       "\n",
       "  Video PublishedAt                                        Video Title  \n",
       "0        2021-11-12  For Facts Sake: Nearly A Million Children Vacc...  \n",
       "1        2021-11-12  For Facts Sake: Nearly A Million Children Vacc...  \n",
       "2        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...  \n",
       "3        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex_df = pd.concat([msnbc11_df1[:2], msnbc12_df1[:2]], axis=0, ignore_index=True)\n",
    "ex_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fox_dfs = [fox3_df,fox4_df,fox5_df,fox6_df,fox7_df,fox8_df,fox9_df,fox10_df,fox11_df,fox12_df,fox1_df1,fox2_df1,fox3_df1,fox4_df1,fox5_df1,fox6_df1,fox7_df1,fox8_df1,fox9_df1,fox10_df1,fox11_df1,fox12_df1]\n",
    "fox_df = pd.concat(fox_dfs, ignore_index=True)\n",
    "fox_df['Channel'] = 'Fox'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "abc_dfs = [abc1_df, abc2_df, abc3_df,abc4_df,abc5_df, abc6_df,abc7_df,abc8_df,abc9_df,abc10_df,abc11_df,abc12_df,abc1_df1,abc2_df1,abc3_df1,abc4_df1,abc5_df1,abc6_df1,abc7_df1,abc8_df1,abc9_df1,abc10_df1,abc11_df1,abc12_df1]\n",
    "abc_df = pd.concat(abc_dfs, ignore_index=True)\n",
    "abc_df['Channel'] = 'ABC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "msnbc_dfs = [msnbc2_df, msnbc3_df,msnbc4_df,msnbc5_df,msnbc6_df,msnbc7_df,msnbc8_df,msnbc9_df,msnbc10_df,msnbc11_df,msnbc12_df,msnbc1_df1,msnbc2_df1,msnbc3_df1,msnbc4_df1,msnbc5_df1,msnbc6_df1,msnbc7_df1,msnbc8_df1,msnbc9_df1,msnbc10_df1,msnbc11_df1,msnbc12_df1]\n",
    "msnbc_df = pd.concat(msnbc_dfs, ignore_index=True)\n",
    "msnbc_df['Channel'] = 'MSNBC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "139587\n",
      "139587\n"
     ]
    }
   ],
   "source": [
    "combined_df = pd.concat([fox_df, abc_df, msnbc_df], axis=0, ignore_index=True)\n",
    "print(len(fox_df)+len(abc_df)+len(msnbc_df))\n",
    "print(len(combined_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "def tokenize_normalize(comment):\n",
    "    '''lowercasing, removing punctuation, and using lemma of each token'''\n",
    "    doc = nlp(comment)\n",
    "    normalized_tokens = [token.lemma_.lower() for token in doc if not token.is_punct]\n",
    "    return normalized_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c787a6d90e95411cb472b79790cdde13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tokenizing and Normalizing Comments:   0%|          | 0/139587 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tqdm.pandas(desc=\"Tokenizing and Normalizing Comments\")\n",
    "# combined_df['Normalized_Comments'] = combined_df['Comment'].progress_apply(tokenize_normalize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering: K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frozenset({'39',\n",
       "           'a',\n",
       "           'about',\n",
       "           'above',\n",
       "           'across',\n",
       "           'after',\n",
       "           'afterwards',\n",
       "           'again',\n",
       "           'against',\n",
       "           'all',\n",
       "           'almost',\n",
       "           'alone',\n",
       "           'along',\n",
       "           'already',\n",
       "           'also',\n",
       "           'although',\n",
       "           'always',\n",
       "           'am',\n",
       "           'among',\n",
       "           'amongst',\n",
       "           'amoungst',\n",
       "           'amount',\n",
       "           'an',\n",
       "           'and',\n",
       "           'another',\n",
       "           'any',\n",
       "           'anyhow',\n",
       "           'anyone',\n",
       "           'anything',\n",
       "           'anyway',\n",
       "           'anywhere',\n",
       "           'are',\n",
       "           'around',\n",
       "           'as',\n",
       "           'at',\n",
       "           'back',\n",
       "           'be',\n",
       "           'became',\n",
       "           'because',\n",
       "           'become',\n",
       "           'becomes',\n",
       "           'becoming',\n",
       "           'been',\n",
       "           'before',\n",
       "           'beforehand',\n",
       "           'behind',\n",
       "           'being',\n",
       "           'below',\n",
       "           'beside',\n",
       "           'besides',\n",
       "           'between',\n",
       "           'beyond',\n",
       "           'bill',\n",
       "           'both',\n",
       "           'bottom',\n",
       "           'br',\n",
       "           'but',\n",
       "           'by',\n",
       "           'call',\n",
       "           'can',\n",
       "           'cannot',\n",
       "           'cant',\n",
       "           'co',\n",
       "           'com',\n",
       "           'con',\n",
       "           'could',\n",
       "           'couldnt',\n",
       "           'cry',\n",
       "           'de',\n",
       "           'describe',\n",
       "           'detail',\n",
       "           'do',\n",
       "           'done',\n",
       "           'down',\n",
       "           'due',\n",
       "           'during',\n",
       "           'each',\n",
       "           'eg',\n",
       "           'eight',\n",
       "           'either',\n",
       "           'eleven',\n",
       "           'else',\n",
       "           'elsewhere',\n",
       "           'empty',\n",
       "           'enough',\n",
       "           'etc',\n",
       "           'even',\n",
       "           'ever',\n",
       "           'every',\n",
       "           'everyone',\n",
       "           'everything',\n",
       "           'everywhere',\n",
       "           'except',\n",
       "           'few',\n",
       "           'fifteen',\n",
       "           'fifty',\n",
       "           'fill',\n",
       "           'find',\n",
       "           'fire',\n",
       "           'first',\n",
       "           'five',\n",
       "           'for',\n",
       "           'former',\n",
       "           'formerly',\n",
       "           'forty',\n",
       "           'found',\n",
       "           'four',\n",
       "           'from',\n",
       "           'front',\n",
       "           'full',\n",
       "           'further',\n",
       "           'get',\n",
       "           'give',\n",
       "           'go',\n",
       "           'had',\n",
       "           'has',\n",
       "           'hasnt',\n",
       "           'have',\n",
       "           'he',\n",
       "           'hence',\n",
       "           'her',\n",
       "           'here',\n",
       "           'hereafter',\n",
       "           'hereby',\n",
       "           'herein',\n",
       "           'hereupon',\n",
       "           'hers',\n",
       "           'herself',\n",
       "           'him',\n",
       "           'himself',\n",
       "           'his',\n",
       "           'how',\n",
       "           'however',\n",
       "           'http',\n",
       "           'https',\n",
       "           'hundred',\n",
       "           'i',\n",
       "           'ie',\n",
       "           'if',\n",
       "           'in',\n",
       "           'inc',\n",
       "           'indeed',\n",
       "           'interest',\n",
       "           'into',\n",
       "           'io',\n",
       "           'is',\n",
       "           'it',\n",
       "           'its',\n",
       "           'itself',\n",
       "           'keep',\n",
       "           'last',\n",
       "           'latter',\n",
       "           'latterly',\n",
       "           'least',\n",
       "           'less',\n",
       "           'll',\n",
       "           'ltd',\n",
       "           'made',\n",
       "           'many',\n",
       "           'may',\n",
       "           'me',\n",
       "           'meanwhile',\n",
       "           'might',\n",
       "           'mill',\n",
       "           'mine',\n",
       "           'more',\n",
       "           'moreover',\n",
       "           'most',\n",
       "           'mostly',\n",
       "           'move',\n",
       "           'much',\n",
       "           'must',\n",
       "           'my',\n",
       "           'myself',\n",
       "           'name',\n",
       "           'namely',\n",
       "           'neither',\n",
       "           'net',\n",
       "           'never',\n",
       "           'nevertheless',\n",
       "           'next',\n",
       "           'nine',\n",
       "           'no',\n",
       "           'nobody',\n",
       "           'none',\n",
       "           'noone',\n",
       "           'nor',\n",
       "           'not',\n",
       "           'nothing',\n",
       "           'now',\n",
       "           'nowhere',\n",
       "           'of',\n",
       "           'off',\n",
       "           'often',\n",
       "           'on',\n",
       "           'once',\n",
       "           'one',\n",
       "           'only',\n",
       "           'onto',\n",
       "           'or',\n",
       "           'org',\n",
       "           'other',\n",
       "           'others',\n",
       "           'otherwise',\n",
       "           'our',\n",
       "           'ours',\n",
       "           'ourselves',\n",
       "           'out',\n",
       "           'over',\n",
       "           'own',\n",
       "           'part',\n",
       "           'per',\n",
       "           'perhaps',\n",
       "           'please',\n",
       "           'put',\n",
       "           'rather',\n",
       "           're',\n",
       "           'same',\n",
       "           'see',\n",
       "           'seem',\n",
       "           'seemed',\n",
       "           'seeming',\n",
       "           'seems',\n",
       "           'serious',\n",
       "           'several',\n",
       "           'she',\n",
       "           'should',\n",
       "           'show',\n",
       "           'side',\n",
       "           'since',\n",
       "           'sincere',\n",
       "           'six',\n",
       "           'sixty',\n",
       "           'so',\n",
       "           'some',\n",
       "           'somehow',\n",
       "           'someone',\n",
       "           'something',\n",
       "           'sometime',\n",
       "           'sometimes',\n",
       "           'somewhere',\n",
       "           'still',\n",
       "           'such',\n",
       "           'system',\n",
       "           'take',\n",
       "           'ten',\n",
       "           'than',\n",
       "           'that',\n",
       "           'the',\n",
       "           'their',\n",
       "           'them',\n",
       "           'themselves',\n",
       "           'then',\n",
       "           'thence',\n",
       "           'there',\n",
       "           'thereafter',\n",
       "           'thereby',\n",
       "           'therefore',\n",
       "           'therein',\n",
       "           'thereupon',\n",
       "           'these',\n",
       "           'they',\n",
       "           'thick',\n",
       "           'thin',\n",
       "           'third',\n",
       "           'this',\n",
       "           'those',\n",
       "           'though',\n",
       "           'three',\n",
       "           'through',\n",
       "           'throughout',\n",
       "           'thru',\n",
       "           'thus',\n",
       "           'to',\n",
       "           'together',\n",
       "           'too',\n",
       "           'top',\n",
       "           'toward',\n",
       "           'towards',\n",
       "           'twelve',\n",
       "           'twenty',\n",
       "           'two',\n",
       "           'un',\n",
       "           'under',\n",
       "           'until',\n",
       "           'up',\n",
       "           'upon',\n",
       "           'us',\n",
       "           'very',\n",
       "           'via',\n",
       "           'was',\n",
       "           'we',\n",
       "           'well',\n",
       "           'were',\n",
       "           'what',\n",
       "           'whatever',\n",
       "           'when',\n",
       "           'whence',\n",
       "           'whenever',\n",
       "           'where',\n",
       "           'whereafter',\n",
       "           'whereas',\n",
       "           'whereby',\n",
       "           'wherein',\n",
       "           'whereupon',\n",
       "           'wherever',\n",
       "           'whether',\n",
       "           'which',\n",
       "           'while',\n",
       "           'whither',\n",
       "           'who',\n",
       "           'whoever',\n",
       "           'whole',\n",
       "           'whom',\n",
       "           'whose',\n",
       "           'why',\n",
       "           'will',\n",
       "           'with',\n",
       "           'within',\n",
       "           'without',\n",
       "           'would',\n",
       "           'www',\n",
       "           'yet',\n",
       "           'you',\n",
       "           'your',\n",
       "           'yours',\n",
       "           'yourself',\n",
       "           'yourselves'})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS\n",
    "custom_stop_words = ['http', 'https', 'www', 'com', 'net', 'org', 'io', '39', 'br', 'll'] # customize stop words\n",
    "\n",
    "combined_stop_words = ENGLISH_STOP_WORDS.union(custom_stop_words)\n",
    "combined_stop_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS\n",
    "\n",
    "custom_stop_words = ['http', 'https', 'www', 'com', 'net', 'org', 'io', '39', 'br', 'll'] # customize stop words\n",
    "combined_stop_words = list(ENGLISH_STOP_WORDS.union(custom_stop_words))\n",
    "\n",
    "# Initialize the vectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer(\n",
    "    max_df=0.8, \n",
    "    max_features=20000,\n",
    "    min_df=5, \n",
    "    stop_words=combined_stop_words,\n",
    "    norm='l2'\n",
    ")\n",
    "\n",
    "# Train the vectorizer on the combined corpus\n",
    "newsTFVects = tfidf_vectorizer.fit_transform(combined_df['Comment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139587, 15679)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newsTFVects.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import MiniBatchKMeans\n",
    "\n",
    "def find_optimal_clusters(data, max_k):\n",
    "    ''' \n",
    "    Plot the Sum of Squared Errors (SSE) for a range of cluster sizes and look \n",
    "    for the \"elbow\" where the SSE begins to level off\n",
    "    '''\n",
    "\n",
    "    iters = range(2, max_k+1, 2)\n",
    "    \n",
    "    sse = []\n",
    "    for k in iters:\n",
    "        sse.append(MiniBatchKMeans(n_clusters=k, init_size=1024, batch_size=2048, random_state=20).fit(data).inertia_)\n",
    "        print('Fit {} clusters'.format(k))\n",
    "        \n",
    "    f, ax = plt.subplots(1, 1)\n",
    "    ax.plot(iters, sse, marker='o')\n",
    "    ax.set_xlabel('Cluster Centers')\n",
    "    ax.set_xticks(iters)\n",
    "    ax.set_xticklabels(iters)\n",
    "    ax.set_ylabel('SSE')\n",
    "    ax.set_title('SSE by Cluster Center Plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit 2 clusters\n",
      "Fit 4 clusters\n",
      "Fit 6 clusters\n",
      "Fit 8 clusters\n",
      "Fit 10 clusters\n",
      "Fit 12 clusters\n",
      "Fit 14 clusters\n",
      "Fit 16 clusters\n",
      "Fit 18 clusters\n",
      "Fit 20 clusters\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_optimal_clusters(newsTFVects, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit 2 clusters\n",
      "Fit 4 clusters\n",
      "Fit 6 clusters\n",
      "Fit 8 clusters\n",
      "Fit 10 clusters\n",
      "Fit 12 clusters\n",
      "Fit 14 clusters\n",
      "Fit 16 clusters\n",
      "Fit 18 clusters\n",
      "Fit 20 clusters\n",
      "Fit 22 clusters\n",
      "Fit 24 clusters\n",
      "Fit 26 clusters\n",
      "Fit 28 clusters\n",
      "Fit 30 clusters\n",
      "Fit 32 clusters\n",
      "Fit 34 clusters\n",
      "Fit 36 clusters\n",
      "Fit 38 clusters\n",
      "Fit 40 clusters\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_optimal_clusters(newsTFVects, 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the elbow method, the best clustering number is 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=10)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=10)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numClusters = 10\n",
    "km = sklearn.cluster.KMeans(n_clusters=numClusters, init='k-means++')\n",
    "km.fit(newsTFVects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top terms per cluster:\n",
      "Cluster 0:\n",
      " vaccine\n",
      " virus\n",
      " covid\n",
      " people\n",
      " don\n",
      " gates\n",
      " just\n",
      " getting\n",
      " make\n",
      " vaccinated\n",
      "\n",
      "\n",
      "Cluster 1:\n",
      " people\n",
      " virus\n",
      " like\n",
      " just\n",
      " covid\n",
      " vaccinated\n",
      " need\n",
      " world\n",
      " think\n",
      " know\n",
      "\n",
      "\n",
      "Cluster 2:\n",
      " going\n",
      " people\n",
      " just\n",
      " virus\n",
      " like\n",
      " know\n",
      " biden\n",
      " trump\n",
      " happen\n",
      " think\n",
      "\n",
      "\n",
      "Cluster 3:\n",
      " florida\n",
      " desantis\n",
      " governor\n",
      " state\n",
      " people\n",
      " covid\n",
      " don\n",
      " texas\n",
      " live\n",
      " ron\n",
      "\n",
      "\n",
      "Cluster 4:\n",
      " trump\n",
      " president\n",
      " 2020\n",
      " biden\n",
      " gates\n",
      " obama\n",
      " 2015\n",
      " donald\n",
      " did\n",
      " like\n",
      "\n",
      "\n",
      "Cluster 5:\n",
      " virus\n",
      " china\n",
      " gates\n",
      " just\n",
      " like\n",
      " biden\n",
      " covid\n",
      " world\n",
      " know\n",
      " right\n",
      "\n",
      "\n",
      "Cluster 6:\n",
      " news\n",
      " fox\n",
      " fake\n",
      " cnn\n",
      " media\n",
      " trump\n",
      " just\n",
      " like\n",
      " msnbc\n",
      " faux\n",
      "\n",
      "\n",
      "Cluster 7:\n",
      " don\n",
      " know\n",
      " want\n",
      " people\n",
      " trust\n",
      " believe\n",
      " just\n",
      " care\n",
      " like\n",
      " think\n",
      "\n",
      "\n",
      "Cluster 8:\n",
      " lol\n",
      " trump\n",
      " right\n",
      " like\n",
      " just\n",
      " virus\n",
      " people\n",
      " know\n",
      " don\n",
      " think\n",
      "\n",
      "\n",
      "Cluster 9:\n",
      " quot\n",
      " people\n",
      " trump\n",
      " just\n",
      " virus\n",
      " like\n",
      " covid\n",
      " don\n",
      " said\n",
      " know\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "terms = tfidf_vectorizer.get_feature_names_out()\n",
    "print(\"Top terms per cluster:\")\n",
    "order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n",
    "for i in range(numClusters):\n",
    "    print(\"Cluster %d:\" % i)\n",
    "    for ind in order_centroids[i, :10]:\n",
    "        print(' %s' % terms[ind])\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling: LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LatentDirichletAllocation(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LatentDirichletAllocation<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html\">?<span>Documentation for LatentDirichletAllocation</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LatentDirichletAllocation(random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LatentDirichletAllocation(random_state=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda = LatentDirichletAllocation(n_components=10, random_state=0)\n",
    "lda.fit(newsTFVects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #1:\n",
      "quot thank tucker wow like evidence trump just believe say\n",
      "\n",
      "\n",
      "Topic #2:\n",
      "news fox fake lol cnn media msnbc bs evil joke\n",
      "\n",
      "\n",
      "Topic #3:\n",
      "people virus quot world gates let population brandon control don\n",
      "\n",
      "\n",
      "Topic #4:\n",
      "trump virus china gates obama 2020 knew lab did fauci\n",
      "\n",
      "\n",
      "Topic #5:\n",
      "quot good vaccine know covid agree people test like gates\n",
      "\n",
      "\n",
      "Topic #6:\n",
      "president biden trump god bless america joe people gates amen\n",
      "\n",
      "\n",
      "Topic #7:\n",
      "border yes dislike war jesus true open god people biden\n",
      "\n",
      "\n",
      "Topic #8:\n",
      "youtu exactly woman brave vaccinated hope vaccine variant right liar\n",
      "\n",
      "\n",
      "Topic #9:\n",
      "mask people don masks wear trump quot just want democrats\n",
      "\n",
      "\n",
      "Topic #10:\n",
      "youtube watch href vaccine amp covid people immune quot rate\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_topics(model, count_vectorizer, n_top_words):\n",
    "    words = count_vectorizer.get_feature_names_out()\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(f\"Topic #{topic_idx+1}:\")\n",
    "        print(\" \".join([words[i] for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
    "        print(\"\\n\")\n",
    "\n",
    "print_topics(lda, tfidf_vectorizer, n_top_words=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03a1203cdcf545689cef3d159f5497da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tokenizing and normalizing comments:   0%|          | 0/139587 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9308eb4823244fd08dae93bd4a3be907",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tokenizing and normalizing comments:   0%|          | 0/139587 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/itsyuzhou/opt/anaconda3/lib/python3.9/site-packages/spacy/pipeline/lemmatizer.py:211: UserWarning: [W108] The rule-based lemmatizer did not find POS annotation for one or more tokens. Check that your pipeline includes components that assign token.pos, typically 'tagger'+'attribute_ruler' or 'morphologizer'.\n",
      "  warnings.warn(Warnings.W108)\n"
     ]
    }
   ],
   "source": [
    "import lucem_illud\n",
    "\n",
    "tqdm.pandas(desc=\"Tokenizing and normalizing comments\")\n",
    "combined_df['tokenized_text'] = combined_df['Comment'].progress_apply(lambda x: lucem_illud.word_tokenize(x))\n",
    "combined_df['normalized_tokens'] = combined_df['tokenized_text'].progress_apply(lambda x: lucem_illud.normalizeTokens(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comment PublishedAt</th>\n",
       "      <th>Comment</th>\n",
       "      <th>Video PublishedAt</th>\n",
       "      <th>Video Title</th>\n",
       "      <th>Channel</th>\n",
       "      <th>tokenized_text</th>\n",
       "      <th>normalized_tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-09-12</td>\n",
       "      <td>Pathetic fear mongering</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[Pathetic, fear, mongering]</td>\n",
       "      <td>[pathetic, fear, mongering]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-04-30</td>\n",
       "      <td>Prophetic</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[Prophetic]</td>\n",
       "      <td>[prophetic]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-03-16</td>\n",
       "      <td>Hey! First comment of 2023! Is it anniversary ...</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[Hey, First, comment, of, 2023, Is, it, annive...</td>\n",
       "      <td>[hey, comment, anniversary, day, china, virus]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-09-12</td>\n",
       "      <td>It&amp;#39;s kind of surreal to watch this now giv...</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[It&amp;#39;s, kind, of, surreal, to, watch, this,...</td>\n",
       "      <td>[it&amp;#39;s, kind, surreal, watch, given, tucker...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-07-10</td>\n",
       "      <td>I know I am 2 years late, but when did Tucker ...</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[I, know, I, am, 2, years, late, but, when, di...</td>\n",
       "      <td>[know, years, late, tucker, trust, science]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139582</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>@@pohakumana4288 &lt;br&gt;&lt;br&gt;We are talking about PR</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[@@pohakumana4288, &lt;, br&gt;&lt;br, &gt;, We, are, talk...</td>\n",
       "      <td>[@@pohakumana4288, &lt;, br&gt;&lt;br, &gt;, talking, pr]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139583</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>@@jlock9085 are you? because no one anywhere e...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[@@jlock9085, are, you, because, no, one, anyw...</td>\n",
       "      <td>[@@jlock9085, matters]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139584</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Dislike</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[Dislike]</td>\n",
       "      <td>[dislike]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139585</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>from John Campbell today,  UK confirmed 159,43...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[from, John, Campbell, today, UK, confirmed, 1...</td>\n",
       "      <td>[john, campbell, today, uk, confirmed, omicron...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139586</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>I believe 50 percent positive actually, but ma...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[I, believe, 50, percent, positive, actually, ...</td>\n",
       "      <td>[believe, percent, positive, actually, cold, l...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139587 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Comment PublishedAt                                            Comment  \\\n",
       "0               2023-09-12                            Pathetic fear mongering   \n",
       "1               2023-04-30                                          Prophetic   \n",
       "2               2023-03-16  Hey! First comment of 2023! Is it anniversary ...   \n",
       "3               2022-09-12  It&#39;s kind of surreal to watch this now giv...   \n",
       "4               2022-07-10  I know I am 2 years late, but when did Tucker ...   \n",
       "...                    ...                                                ...   \n",
       "139582          2021-12-28   @@pohakumana4288 <br><br>We are talking about PR   \n",
       "139583          2021-12-28  @@jlock9085 are you? because no one anywhere e...   \n",
       "139584          2021-12-28                                            Dislike   \n",
       "139585          2021-12-28  from John Campbell today,  UK confirmed 159,43...   \n",
       "139586          2021-12-28  I believe 50 percent positive actually, but ma...   \n",
       "\n",
       "       Video PublishedAt                                        Video Title  \\\n",
       "0             2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "1             2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "2             2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "3             2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "4             2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "...                  ...                                                ...   \n",
       "139582        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139583        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139584        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139585        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139586        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "\n",
       "       Channel                                     tokenized_text  \\\n",
       "0          Fox                        [Pathetic, fear, mongering]   \n",
       "1          Fox                                        [Prophetic]   \n",
       "2          Fox  [Hey, First, comment, of, 2023, Is, it, annive...   \n",
       "3          Fox  [It&#39;s, kind, of, surreal, to, watch, this,...   \n",
       "4          Fox  [I, know, I, am, 2, years, late, but, when, di...   \n",
       "...        ...                                                ...   \n",
       "139582   MSNBC  [@@pohakumana4288, <, br><br, >, We, are, talk...   \n",
       "139583   MSNBC  [@@jlock9085, are, you, because, no, one, anyw...   \n",
       "139584   MSNBC                                          [Dislike]   \n",
       "139585   MSNBC  [from, John, Campbell, today, UK, confirmed, 1...   \n",
       "139586   MSNBC  [I, believe, 50, percent, positive, actually, ...   \n",
       "\n",
       "                                        normalized_tokens  \n",
       "0                             [pathetic, fear, mongering]  \n",
       "1                                             [prophetic]  \n",
       "2          [hey, comment, anniversary, day, china, virus]  \n",
       "3       [it&#39;s, kind, surreal, watch, given, tucker...  \n",
       "4             [know, years, late, tucker, trust, science]  \n",
       "...                                                   ...  \n",
       "139582      [@@pohakumana4288, <, br><br, >, talking, pr]  \n",
       "139583                             [@@jlock9085, matters]  \n",
       "139584                                          [dislike]  \n",
       "139585  [john, campbell, today, uk, confirmed, omicron...  \n",
       "139586  [believe, percent, positive, actually, cold, l...  \n",
       "\n",
       "[139587 rows x 7 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dropMissing(wordLst, vocab):\n",
    "    return [w for w in wordLst if w in vocab]\n",
    "\n",
    "combined_df['reduced_tokens'] = combined_df['normalized_tokens'].apply(lambda x: dropMissing(x, tfidf_vectorizer.vocabulary_.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = gensim.corpora.Dictionary(combined_df['reduced_tokens'])\n",
    "corpus = [dictionary.doc2bow(text) for text in combined_df['reduced_tokens']]\n",
    "\n",
    "gensim.corpora.MmCorpus.serialize('comment.mm', corpus)\n",
    "senmm = gensim.corpora.MmCorpus('comment.mm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "senlda = gensim.models.ldamodel.LdaModel(corpus=senmm, id2word=dictionary, num_topics=10, alpha='auto', eta='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ldaDF = pd.DataFrame({\n",
    "        'comment' : combined_df['Comment'],\n",
    "        'topics' : [senlda[dictionary.doc2bow(l)] for l in combined_df['reduced_tokens']]\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic_0</th>\n",
       "      <th>Topic_1</th>\n",
       "      <th>Topic_2</th>\n",
       "      <th>Topic_3</th>\n",
       "      <th>Topic_4</th>\n",
       "      <th>Topic_5</th>\n",
       "      <th>Topic_6</th>\n",
       "      <th>Topic_7</th>\n",
       "      <th>Topic_8</th>\n",
       "      <th>Topic_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>like</td>\n",
       "      <td>trump</td>\n",
       "      <td>virus</td>\n",
       "      <td>biden</td>\n",
       "      <td>desantis</td>\n",
       "      <td>gates</td>\n",
       "      <td>news</td>\n",
       "      <td>video</td>\n",
       "      <td>covid</td>\n",
       "      <td>children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>florida</td>\n",
       "      <td>president</td>\n",
       "      <td>people</td>\n",
       "      <td>thank</td>\n",
       "      <td>world</td>\n",
       "      <td>people</td>\n",
       "      <td>msnbc</td>\n",
       "      <td>good</td>\n",
       "      <td>vaccinated</td>\n",
       "      <td>vaccine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>evil</td>\n",
       "      <td>people</td>\n",
       "      <td>mask</td>\n",
       "      <td>joe</td>\n",
       "      <td>china</td>\n",
       "      <td>kids</td>\n",
       "      <td>media</td>\n",
       "      <td>child</td>\n",
       "      <td>people</td>\n",
       "      <td>know</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>little</td>\n",
       "      <td>covid</td>\n",
       "      <td>death</td>\n",
       "      <td>jesus</td>\n",
       "      <td>state</td>\n",
       "      <td>vaccines</td>\n",
       "      <td>stupid</td>\n",
       "      <td>yes</td>\n",
       "      <td>dislike</td>\n",
       "      <td>better</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lies</td>\n",
       "      <td>vote</td>\n",
       "      <td>masks</td>\n",
       "      <td>god</td>\n",
       "      <td>pandemic</td>\n",
       "      <td>fauci</td>\n",
       "      <td>lol</td>\n",
       "      <td>parents</td>\n",
       "      <td>god</td>\n",
       "      <td>effects</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>talk</td>\n",
       "      <td>care</td>\n",
       "      <td>vaccine</td>\n",
       "      <td>mini</td>\n",
       "      <td>virus</td>\n",
       "      <td>vaccine</td>\n",
       "      <td>fake</td>\n",
       "      <td>youtube</td>\n",
       "      <td>amp</td>\n",
       "      <td>shot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ron</td>\n",
       "      <td>term</td>\n",
       "      <td>deaths</td>\n",
       "      <td>heart</td>\n",
       "      <td>america</td>\n",
       "      <td>money</td>\n",
       "      <td>facts</td>\n",
       "      <td>event</td>\n",
       "      <td>fear</td>\n",
       "      <td>covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sad</td>\n",
       "      <td>years</td>\n",
       "      <td>cases</td>\n",
       "      <td>earth</td>\n",
       "      <td>country</td>\n",
       "      <td>need</td>\n",
       "      <td>guy</td>\n",
       "      <td>look</td>\n",
       "      <td>life</td>\n",
       "      <td>getting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bad</td>\n",
       "      <td>obama</td>\n",
       "      <td>year</td>\n",
       "      <td>survival</td>\n",
       "      <td>government</td>\n",
       "      <td>nt</td>\n",
       "      <td>truth</td>\n",
       "      <td>read</td>\n",
       "      <td>hope</td>\n",
       "      <td>exactly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>afraid</td>\n",
       "      <td>dead</td>\n",
       "      <td>sick</td>\n",
       "      <td>texas</td>\n",
       "      <td>party</td>\n",
       "      <td>want</td>\n",
       "      <td>believe</td>\n",
       "      <td>seen</td>\n",
       "      <td>die</td>\n",
       "      <td>got</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Topic_0    Topic_1  Topic_2   Topic_3     Topic_4   Topic_5  Topic_6  \\\n",
       "0     like      trump    virus     biden    desantis     gates     news   \n",
       "1  florida  president   people     thank       world    people    msnbc   \n",
       "2     evil     people     mask       joe       china      kids    media   \n",
       "3   little      covid    death     jesus       state  vaccines   stupid   \n",
       "4     lies       vote    masks       god    pandemic     fauci      lol   \n",
       "5     talk       care  vaccine      mini       virus   vaccine     fake   \n",
       "6      ron       term   deaths     heart     america     money    facts   \n",
       "7      sad      years    cases     earth     country      need      guy   \n",
       "8      bad      obama     year  survival  government        nt    truth   \n",
       "9   afraid       dead     sick     texas       party      want  believe   \n",
       "\n",
       "   Topic_7     Topic_8   Topic_9  \n",
       "0    video       covid  children  \n",
       "1     good  vaccinated   vaccine  \n",
       "2    child      people      know  \n",
       "3      yes     dislike    better  \n",
       "4  parents         god   effects  \n",
       "5  youtube         amp      shot  \n",
       "6    event        fear     covid  \n",
       "7     look        life   getting  \n",
       "8     read        hope   exactly  \n",
       "9     seen         die       got  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topicsDict = {}\n",
    "for topicNum in range(senlda.num_topics):\n",
    "    topicWords = [w for w, p in senlda.show_topic(topicNum)]\n",
    "    topicsDict['Topic_{}'.format(topicNum)] = topicWords\n",
    "\n",
    "wordRanksDF = pd.DataFrame(topicsDict)\n",
    "wordRanksDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sinophobia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "china_keywords = ['china', 'chinese', 'wuhan', 'ccp', 'kungflu', 'kung flu', \n",
    "                  'wuflu', 'chinesevirus', 'wuhanflu', 'chinazi', 'chink', 'sars', \n",
    "                  'communist', 'communism', 'bat', 'bioweapon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8c41413a2144d41bd2215f83318b5d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tokenizing and normalizing comments:   0%|          | 0/139587 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "china_keywords_mask = combined_df['normalized_tokens'].progress_apply(lambda tokens: any(keyword in tokens for keyword in china_keywords))\n",
    "china_df = combined_df[china_keywords_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comment PublishedAt</th>\n",
       "      <th>Comment</th>\n",
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       "      <th>Video Title</th>\n",
       "      <th>Channel</th>\n",
       "      <th>tokenized_text</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-03-16</td>\n",
       "      <td>Hey! First comment of 2023! Is it anniversary ...</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[Hey, First, comment, of, 2023, Is, it, annive...</td>\n",
       "      <td>[hey, comment, anniversary, day, china, virus]</td>\n",
       "      <td>[hey, comment, day, china, virus]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-03-26</td>\n",
       "      <td>Lora of good warnings here... that unfortunate...</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[Lora, of, good, warnings, here, that, unfortu...</td>\n",
       "      <td>[lora, good, warnings, unfortunately, true, sa...</td>\n",
       "      <td>[good, warnings, unfortunately, true, saying, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>The Chinese Coronavirus.</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[The, Chinese, Coronavirus]</td>\n",
       "      <td>[chinese, coronavirus]</td>\n",
       "      <td>[chinese, coronavirus]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>I bet his favorite food is Chinese food. Gtfoh</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[I, bet, his, favorite, food, is, Chinese, foo...</td>\n",
       "      <td>[bet, favorite, food, chinese, food, gtfoh]</td>\n",
       "      <td>[bet, favorite, food, chinese, food, gtfoh]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-05-19</td>\n",
       "      <td>Did he just said that China is the most racist...</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[Did, he, just, said, that, China, is, the, mo...</td>\n",
       "      <td>[said, china, racist, country, world]</td>\n",
       "      <td>[said, china, racist, country, world]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139445</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>American pandemic experts?        US 5 day iso...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[American, pandemic, experts, US, 5, day, isol...</td>\n",
       "      <td>[american, pandemic, experts, day, isolation, ...</td>\n",
       "      <td>[american, pandemic, experts, day, isolation, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139506</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>The great news as they will get natural immuni...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[The, great, news, as, they, will, get, natura...</td>\n",
       "      <td>[great, news, natural, immunity, won&amp;#39;t, ne...</td>\n",
       "      <td>[great, news, natural, immunity, need, communi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139535</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Trump Trump Trump Trump Trump Trump and Cathol...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[Trump, Trump, Trump, Trump, Trump, Trump, and...</td>\n",
       "      <td>[trump, trump, trump, trump, trump, trump, cat...</td>\n",
       "      <td>[trump, trump, trump, trump, trump, trump, cat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139540</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Wow that great news and Puerto Rico is so luck...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[Wow, that, great, news, and, Puerto, Rico, is...</td>\n",
       "      <td>[wow, great, news, puerto, rico, lucky, united...</td>\n",
       "      <td>[wow, great, news, puerto, rico, lucky, united...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139579</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>That&amp;#39;s great news and watch how Puerto Ric...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[That&amp;#39;s, great, news, and, watch, how, Pue...</td>\n",
       "      <td>[that&amp;#39;s, great, news, watch, puerto, rico,...</td>\n",
       "      <td>[great, news, watch, puerto, rico, thrives, mo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10901 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Comment PublishedAt                                            Comment  \\\n",
       "2               2023-03-16  Hey! First comment of 2023! Is it anniversary ...   \n",
       "9               2021-03-26  Lora of good warnings here... that unfortunate...   \n",
       "18              2020-06-22                           The Chinese Coronavirus.   \n",
       "21              2020-06-09     I bet his favorite food is Chinese food. Gtfoh   \n",
       "23              2020-05-19  Did he just said that China is the most racist...   \n",
       "...                    ...                                                ...   \n",
       "139445          2021-12-28  American pandemic experts?        US 5 day iso...   \n",
       "139506          2021-12-28  The great news as they will get natural immuni...   \n",
       "139535          2021-12-28  Trump Trump Trump Trump Trump Trump and Cathol...   \n",
       "139540          2021-12-28  Wow that great news and Puerto Rico is so luck...   \n",
       "139579          2021-12-28  That&#39;s great news and watch how Puerto Ric...   \n",
       "\n",
       "       Video PublishedAt                                        Video Title  \\\n",
       "2             2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "9             2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "18            2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "21            2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "23            2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "...                  ...                                                ...   \n",
       "139445        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139506        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139535        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139540        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139579        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "\n",
       "       Channel                                     tokenized_text  \\\n",
       "2          Fox  [Hey, First, comment, of, 2023, Is, it, annive...   \n",
       "9          Fox  [Lora, of, good, warnings, here, that, unfortu...   \n",
       "18         Fox                        [The, Chinese, Coronavirus]   \n",
       "21         Fox  [I, bet, his, favorite, food, is, Chinese, foo...   \n",
       "23         Fox  [Did, he, just, said, that, China, is, the, mo...   \n",
       "...        ...                                                ...   \n",
       "139445   MSNBC  [American, pandemic, experts, US, 5, day, isol...   \n",
       "139506   MSNBC  [The, great, news, as, they, will, get, natura...   \n",
       "139535   MSNBC  [Trump, Trump, Trump, Trump, Trump, Trump, and...   \n",
       "139540   MSNBC  [Wow, that, great, news, and, Puerto, Rico, is...   \n",
       "139579   MSNBC  [That&#39;s, great, news, and, watch, how, Pue...   \n",
       "\n",
       "                                        normalized_tokens  \\\n",
       "2          [hey, comment, anniversary, day, china, virus]   \n",
       "9       [lora, good, warnings, unfortunately, true, sa...   \n",
       "18                                 [chinese, coronavirus]   \n",
       "21            [bet, favorite, food, chinese, food, gtfoh]   \n",
       "23                  [said, china, racist, country, world]   \n",
       "...                                                   ...   \n",
       "139445  [american, pandemic, experts, day, isolation, ...   \n",
       "139506  [great, news, natural, immunity, won&#39;t, ne...   \n",
       "139535  [trump, trump, trump, trump, trump, trump, cat...   \n",
       "139540  [wow, great, news, puerto, rico, lucky, united...   \n",
       "139579  [that&#39;s, great, news, watch, puerto, rico,...   \n",
       "\n",
       "                                           reduced_tokens  \n",
       "2                       [hey, comment, day, china, virus]  \n",
       "9       [good, warnings, unfortunately, true, saying, ...  \n",
       "18                                 [chinese, coronavirus]  \n",
       "21            [bet, favorite, food, chinese, food, gtfoh]  \n",
       "23                  [said, china, racist, country, world]  \n",
       "...                                                   ...  \n",
       "139445  [american, pandemic, experts, day, isolation, ...  \n",
       "139506  [great, news, natural, immunity, need, communi...  \n",
       "139535  [trump, trump, trump, trump, trump, trump, cat...  \n",
       "139540  [wow, great, news, puerto, rico, lucky, united...  \n",
       "139579  [great, news, watch, puerto, rico, thrives, mo...  \n",
       "\n",
       "[10901 rows x 8 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_stop_words = ['http', 'https', 'www', 'com', 'net', 'org', 'io', '39', 'br', 'll'] # customize stop words\n",
    "combined_stop_words = list(ENGLISH_STOP_WORDS.union(custom_stop_words))\n",
    "\n",
    "# Initialize the vectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer(\n",
    "    max_df=0.8, \n",
    "    max_features=10000,\n",
    "    min_df=5, \n",
    "    stop_words=combined_stop_words,\n",
    "    norm='l2'\n",
    ")\n",
    "\n",
    "# Train the vectorizer on the combined corpus\n",
    "newsTFVects = tfidf_vectorizer.fit_transform(china_df['Comment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit 2 clusters\n",
      "Fit 4 clusters\n",
      "Fit 6 clusters\n",
      "Fit 8 clusters\n",
      "Fit 10 clusters\n",
      "Fit 12 clusters\n",
      "Fit 14 clusters\n",
      "Fit 16 clusters\n",
      "Fit 18 clusters\n",
      "Fit 20 clusters\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_optimal_clusters(newsTFVects, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit 2 clusters\n",
      "Fit 4 clusters\n",
      "Fit 6 clusters\n",
      "Fit 8 clusters\n",
      "Fit 10 clusters\n",
      "Fit 12 clusters\n",
      "Fit 14 clusters\n",
      "Fit 16 clusters\n",
      "Fit 18 clusters\n",
      "Fit 20 clusters\n",
      "Fit 22 clusters\n",
      "Fit 24 clusters\n",
      "Fit 26 clusters\n",
      "Fit 28 clusters\n",
      "Fit 30 clusters\n",
      "Fit 32 clusters\n",
      "Fit 34 clusters\n",
      "Fit 36 clusters\n",
      "Fit 38 clusters\n",
      "Fit 40 clusters\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_optimal_clusters(newsTFVects, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=10)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=10)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numClusters = 10\n",
    "km = sklearn.cluster.KMeans(n_clusters=numClusters, init='k-means++')\n",
    "km.fit(newsTFVects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top terms per cluster:\n",
      "Cluster 0:\n",
      " communist\n",
      " party\n",
      " chinese\n",
      " people\n",
      " china\n",
      " news\n",
      " country\n",
      " evil\n",
      " democrats\n",
      " virus\n",
      "\n",
      "\n",
      "Cluster 1:\n",
      " biden\n",
      " china\n",
      " joe\n",
      " trump\n",
      " president\n",
      " communist\n",
      " ccp\n",
      " vote\n",
      " america\n",
      " 2020\n",
      "\n",
      "\n",
      "Cluster 2:\n",
      " nope\n",
      " ccp\n",
      " just\n",
      " user\n",
      " complicated\n",
      " useless\n",
      " china\n",
      " people\n",
      " does\n",
      " literally\n",
      "\n",
      "\n",
      "Cluster 3:\n",
      " china\n",
      " virus\n",
      " came\n",
      " trump\n",
      " blame\n",
      " say\n",
      " like\n",
      " got\n",
      " corona\n",
      " covid\n",
      "\n",
      "\n",
      "Cluster 4:\n",
      " china\n",
      " virus\n",
      " ccp\n",
      " trump\n",
      " people\n",
      " just\n",
      " like\n",
      " don\n",
      " covid\n",
      " did\n",
      "\n",
      "\n",
      "Cluster 5:\n",
      " world\n",
      " china\n",
      " ccp\n",
      " virus\n",
      " war\n",
      " people\n",
      " chinese\n",
      " rest\n",
      " just\n",
      " trump\n",
      "\n",
      "\n",
      "Cluster 6:\n",
      " wuhan\n",
      " lab\n",
      " fauci\n",
      " virus\n",
      " research\n",
      " gates\n",
      " funded\n",
      " obama\n",
      " china\n",
      " dr\n",
      "\n",
      "\n",
      "Cluster 7:\n",
      " chinese\n",
      " government\n",
      " virus\n",
      " people\n",
      " like\n",
      " coronavirus\n",
      " china\n",
      " just\n",
      " ccp\n",
      " did\n",
      "\n",
      "\n",
      "Cluster 8:\n",
      " quot\n",
      " virus\n",
      " china\n",
      " chinese\n",
      " people\n",
      " just\n",
      " trump\n",
      " wuhan\n",
      " world\n",
      " don\n",
      "\n",
      "\n",
      "Cluster 9:\n",
      " ccp\n",
      " virus\n",
      " evil\n",
      " time\n",
      " china\n",
      " war\n",
      " world\n",
      " release\n",
      " called\n",
      " work\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "terms = tfidf_vectorizer.get_feature_names_out()\n",
    "print(\"Top terms per cluster:\")\n",
    "order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n",
    "for i in range(numClusters):\n",
    "    print(\"Cluster %d:\" % i)\n",
    "    for ind in order_centroids[i, :10]:\n",
    "        print(' %s' % terms[ind])\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topic Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = gensim.corpora.Dictionary(china_df['reduced_tokens'])\n",
    "corpus = [dictionary.doc2bow(text) for text in china_df['reduced_tokens']]\n",
    "\n",
    "gensim.corpora.MmCorpus.serialize('comment.mm', corpus)\n",
    "senmm = gensim.corpora.MmCorpus('comment.mm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "senlda = gensim.models.ldamodel.LdaModel(corpus=senmm, id2word=dictionary, num_topics=10, alpha='auto', eta='auto')\n",
    "\n",
    "ldaDF = pd.DataFrame({\n",
    "        'comment' : china_df['Comment'],\n",
    "        'topics' : [senlda[dictionary.doc2bow(l)] for l in china_df['reduced_tokens']]\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic_0</th>\n",
       "      <th>Topic_1</th>\n",
       "      <th>Topic_2</th>\n",
       "      <th>Topic_3</th>\n",
       "      <th>Topic_4</th>\n",
       "      <th>Topic_5</th>\n",
       "      <th>Topic_6</th>\n",
       "      <th>Topic_7</th>\n",
       "      <th>Topic_8</th>\n",
       "      <th>Topic_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gates</td>\n",
       "      <td>china</td>\n",
       "      <td>china</td>\n",
       "      <td>wuhan</td>\n",
       "      <td>virus</td>\n",
       "      <td>china</td>\n",
       "      <td>china</td>\n",
       "      <td>communist</td>\n",
       "      <td>event</td>\n",
       "      <td>china</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wuhan</td>\n",
       "      <td>people</td>\n",
       "      <td>people</td>\n",
       "      <td>lab</td>\n",
       "      <td>china</td>\n",
       "      <td>virus</td>\n",
       "      <td>ccp</td>\n",
       "      <td>chinese</td>\n",
       "      <td>october</td>\n",
       "      <td>chinese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lab</td>\n",
       "      <td>trump</td>\n",
       "      <td>world</td>\n",
       "      <td>news</td>\n",
       "      <td>vaccine</td>\n",
       "      <td>vaccine</td>\n",
       "      <td>trump</td>\n",
       "      <td>party</td>\n",
       "      <td>foundation</td>\n",
       "      <td>virus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fauci</td>\n",
       "      <td>virus</td>\n",
       "      <td>trump</td>\n",
       "      <td>research</td>\n",
       "      <td>world</td>\n",
       "      <td>people</td>\n",
       "      <td>amp</td>\n",
       "      <td>people</td>\n",
       "      <td>china</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>virus</td>\n",
       "      <td>like</td>\n",
       "      <td>like</td>\n",
       "      <td>virus</td>\n",
       "      <td>covid</td>\n",
       "      <td>going</td>\n",
       "      <td>biden</td>\n",
       "      <td>media</td>\n",
       "      <td>wuhan</td>\n",
       "      <td>government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sars</td>\n",
       "      <td>chinese</td>\n",
       "      <td>virus</td>\n",
       "      <td>china</td>\n",
       "      <td>children</td>\n",
       "      <td>trump</td>\n",
       "      <td>virus</td>\n",
       "      <td>china</td>\n",
       "      <td>virus</td>\n",
       "      <td>people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>obama</td>\n",
       "      <td>covid</td>\n",
       "      <td>chinese</td>\n",
       "      <td>function</td>\n",
       "      <td>research</td>\n",
       "      <td>chinese</td>\n",
       "      <td>world</td>\n",
       "      <td>virus</td>\n",
       "      <td>aids</td>\n",
       "      <td>pandemic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>china</td>\n",
       "      <td>flu</td>\n",
       "      <td>country</td>\n",
       "      <td>gain</td>\n",
       "      <td>wuhan</td>\n",
       "      <td>communism</td>\n",
       "      <td>media</td>\n",
       "      <td>quot</td>\n",
       "      <td>hospitalized</td>\n",
       "      <td>covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>coronavirus</td>\n",
       "      <td>dr</td>\n",
       "      <td>think</td>\n",
       "      <td>bat</td>\n",
       "      <td>viruses</td>\n",
       "      <td>msnbc</td>\n",
       "      <td>america</td>\n",
       "      <td>world</td>\n",
       "      <td>board</td>\n",
       "      <td>know</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pandemic</td>\n",
       "      <td>mask</td>\n",
       "      <td>time</td>\n",
       "      <td>fauci</td>\n",
       "      <td>lab</td>\n",
       "      <td>communist</td>\n",
       "      <td>msnbc</td>\n",
       "      <td>growth</td>\n",
       "      <td>scenario</td>\n",
       "      <td>said</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Topic_0  Topic_1  Topic_2   Topic_3   Topic_4    Topic_5  Topic_6  \\\n",
       "0        gates    china    china     wuhan     virus      china    china   \n",
       "1        wuhan   people   people       lab     china      virus      ccp   \n",
       "2          lab    trump    world      news   vaccine    vaccine    trump   \n",
       "3        fauci    virus    trump  research     world     people      amp   \n",
       "4        virus     like     like     virus     covid      going    biden   \n",
       "5         sars  chinese    virus     china  children      trump    virus   \n",
       "6        obama    covid  chinese  function  research    chinese    world   \n",
       "7        china      flu  country      gain     wuhan  communism    media   \n",
       "8  coronavirus       dr    think       bat   viruses      msnbc  america   \n",
       "9     pandemic     mask     time     fauci       lab  communist    msnbc   \n",
       "\n",
       "     Topic_7       Topic_8     Topic_9  \n",
       "0  communist         event       china  \n",
       "1    chinese       october     chinese  \n",
       "2      party    foundation       virus  \n",
       "3     people         china       world  \n",
       "4      media         wuhan  government  \n",
       "5      china         virus      people  \n",
       "6      virus          aids    pandemic  \n",
       "7       quot  hospitalized       covid  \n",
       "8      world         board        know  \n",
       "9     growth      scenario        said  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topicsDict = {}\n",
    "for topicNum in range(senlda.num_topics):\n",
    "    topicWords = [w for w, p in senlda.show_topic(topicNum)]\n",
    "    topicsDict['Topic_{}'.format(topicNum)] = topicWords\n",
    "\n",
    "wordRanksDF = pd.DataFrame(topicsDict)\n",
    "wordRanksDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RoBERTa Hate Speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/itsyuzhou/opt/anaconda3/lib/python3.9/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "/Users/itsyuzhou/opt/anaconda3/lib/python3.9/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "/Users/itsyuzhou/opt/anaconda3/lib/python3.9/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"facebook/roberta-hate-speech-dynabench-r4-target\")\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"facebook/roberta-hate-speech-dynabench-r4-target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "def predict_hate_speech(comments):\n",
    "    inputs = tokenizer(comments, padding=True, truncation=True, max_length=512, return_tensors=\"pt\")\n",
    "    with torch.no_grad():\n",
    "        logits = model(**inputs).logits\n",
    "    probabilities = torch.softmax(logits, dim=-1)\n",
    "    return probabilities[:, 1].tolist()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7b0b254aaa64c21996594641901d9e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Calculate hate speech score:   0%|          | 0/10901 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bz/72zd2hjs5t5drbxxvfh0xhph0000gn/T/ipykernel_85457/1469583430.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  china_df['Hate Speech Score'] = china_df['Comment'].progress_apply(lambda x: predict_hate_speech(x))\n"
     ]
    }
   ],
   "source": [
    "tqdm.pandas(desc=\"Calculate hate speech score\")\n",
    "\n",
    "china_df['Hate Speech Score'] = china_df['Comment'].progress_apply(lambda x: predict_hate_speech(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "china_df.to_csv('sinophobic_comment.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine an optimal hate speech score threshold, randomly sample 30 comments and manually label them with binary 0 or 1, then iteratively test with different threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 :  The problem here is a damned if you do damned if you don&#39;t scenario. I have thought it was possible from the beginning that it was a manufactured virus, I have thought it was possible from the beginning that it was intentionally released. The first hurdle is proving if what she said is true from her research, and trusting that those vetting her research are not biased/influenced/or threatened with a counter agenda. The second hurdle, well, disproving her research does not mean the CCP were not negligible nor complicit, it just means her research does not prove it. But worst of all, what happens if she is right? Do we go to war? Lets say we just do sanctions, how is that justice? Lets say we want justice, that would mean war, and given the military and resources they have gained in the last half a century, how well will that turn out for the world? How much land, resources, politicians, celebrities do they own in the west? How much corrupted tech have they sold into use in the US Military? How much access do they have in our digital infrastructure? I think the best thing to do is ween ourselves and our allies off their manufacturing industry, immediately.\n",
      "1 :  83 dislikes come from Science denial Democrats or Chinese Communist Party officials.\n",
      "2 :  Then why is there a patent registered to Bill Gates for this virus if it originated from China? Disinformation is real. Is necessary.\n",
      "3 :  ONE WORD : CHINA !\n",
      "4 :  Rodricks A “Democrats pampering China”?🤣🤣🤣🤣\n",
      "5 :  Multinational corporations love the Communist Party of China because they want access to that giant Chinese market. They don&#39;t care that the CCP is an evil regime. They accuse Trump of being what the Communist Party of China is. They&#39;re traitors! DE-COUPLE FROM COMMUNIST CHINA!\n",
      "6 :  Remember the Chinese communist party to blame\n",
      "7 :  We all kinda had a clue it was man made, We just didn&#39;t know where from... <br>Biden &amp; democrats has China under their Thumb.. Think about that..\n",
      "8 :  Except the rest of the world has access to the genome of the virus too, and Tucker thinks we&#39;re all stupid enough to think the whole world is lying for China. I guess Fox viewers are this stupid.\n",
      "9 :  @@leomendez3195 not China, who gave the go ahead? Who wants to make vaccine mandatory?  Those leaders are to blame\n",
      "10 :  Because she would give up her good salary, comfortable life, husband and family, prestigious position and other great things for  a US Citizenship.... Are you dumb? Also, the CCP will send someone after her. Is it all worth it for a citizenship? You are not the brightest apple on the tree...\n",
      "11 :  Yes, and it is time that we send the Chinese Communist party the bill. They have to be hold accountable.\n",
      "12 :  And China is licking their lips, the CCP is not the Chinese people but their government.\n",
      "13 :  The Third World War has come, and the Chinese Communist Party has spread the virus to the world!💀☠👺😈👻👽👹☻👿👾🤖💩\n",
      "14 :  35,000,000 in tax dollars given to Wuhan China in 2009 by barckkk Hussein bin laden, then china gave dr Charles lieber 1,800,000 and designed a lab for him. I know probably just a clinsodense.\n",
      "15 :  I&#39;m just wondering, what evidence does she have? If the virus was man made, why not make it more deadly? If China is the source, why would they let it spread in their own country?\n",
      "16 :  @ Just enough This same network was running stories a few months ago about Gates having shady dealings with Epstein... Now they are proppin him up like a prophet... ID 2020 chipping everybody by 2021 that&#39;s his big thing, vaccines for population control, Globalist, ties to powerful people in China, he&#39;s a major contributor to the world health organization... He&#39;s no prophet he&#39;s a puppet master...\n",
      "17 :  I prefer CCP Virus\n",
      "18 :  Bill Gates financed this chinese lab with millions of dollars through his Foundation  and spoke of this virus at an international event in 2015, even using images of it in his presentation! What more is there to say?!<br>Furthermore, Covid19 means Certificate of Vaccination Identification Artificial Inteligence (1=A; 9=I). Ghastly!!\n",
      "19 :  This isnt new. Back in march a youtuber posted that the virus has 6 RNA inserts. Its man made. Problem is there are 2 labs in Wuhan. China and the NIH both had labs in Wuhan. Dr Fauchi has been pushing for gain of function of viruses. Obama had pulled the plug and Dr Fauchi still had it funded. Big money in vaccines. Who has profitted the most from Cov19?\n",
      "20 :  Racist scientist blaming the Chinese!  Oh......wait a minute\n",
      "21 :  According to Judy Mikovitz the virus was made by a group along with anthony faucci who is responsible for it within wuhan lab\n",
      "22 :  the democrats have been purchased by the CCP\n",
      "23 :  This explains why Wuhan P4 Lab is out of limit even for WHO. CCP has tentacles deep in scientific society -- quite a few American senior scientists were indicted for involvement in China&#39;s thousand-people plan. My deepest respect for Dr Limeng Yan for exposing this. She is a hero.\n",
      "24 :  CHINESE BIO-WEAPON. MAN MADE GAIN OF FUNCTION CHIMERA VIRUS. DR. SHI ZHENGLI WUHAN P4 LAB. DR. FAUCI HAS THE RECEIPTS. CRIMES AGAINST HUMANITY. NUREMBERG TRIAL. GALLOWS!\n",
      "25 :  CCP were lying with those numbers, here’s a nurse at Wuhan hospital tells it all <br><br><br>https://youtu.be/NVHFmVccWns\n",
      "26 :  blame china\n",
      "27 :  Sounds like Fauci and the NIH paid for part of it. They believed since they&#39;re restricted here they will fund a Lab where Frankenstein could be born without any regulation at all, especially unbeknownst to these idiots that if it served the CCP and its military it would especially be allowed.\n",
      "28 :  It&#39;s just as likely this virus originated in a Chinese lab and if so it would be covered up with a handy meat market. I would bet lab techs frequently shop at meat markets and I wonder if they became ill before the spread of this virus.\n",
      "29 :  Yes she is correct but before Wuhan it was developed at Fort Detrick and passed on to Wuhan labs. <br>George Webb has been saying this for some time already.<br>About four or five weeks ago all of his work, many years worth, was scrubbed from YouTube. You can no longer find him on YouTube under his own name but he can be found on other platforms.\n",
      "30 :  They won&#39;t--just watch. Our corporate rulers benefit handsomely from war, and if they can&#39;t get one with Iran or Russia, they will have one with China. Other major benefit: the American public is becoming conditioned to give up their Constitutional rights based on a mix of propaganda and poorly executed science...or whenever the government says we &quot;are in danger.&quot;\n",
      "31 :  we already know whaat wuhan lab was doing for the CDC aka Fauci.....this was known  a year ago....time for you and your shepherd MSNBC to catch up\n",
      "32 :  I guess Puerto Ricans get runny noses too. Don&#39;t worry. Communism will that care of it.\n",
      "33 :  His administration continues to attack us Americans!!! Communist president dictator! 🤬\n",
      "34 :  Some countries have already tried to sue China but were unsuccessful\n",
      "35 :  Stop, you CCP troll. Here is 50 cents! GO AWAY!\n",
      "36 :  Everyone in South Korea and China wore masks and both countries disinfected streets and sidewalks.\n",
      "37 :  The answer is China is at fault.\n",
      "38 :  She needs to be protected by China.\n",
      "39 :  Imagine how brave this woman is, given WHERE she&#39;s from?! Love her!!! I wonder if she also knows that this particular virus began its journey at a University in North Carolina and was then outsourced to China during the Obama administration via Anthony Fauci, along with a cool $3.7 million to continue the &quot;chimeric construction&quot; to give the it &quot;gain of function&quot; (turbo-charge it)??\n",
      "40 :  MSNBC you only speak to fools who do not bother to seek the truth. Bill Gates is 100% behind this pandemic and you hail him as some prophetic genius. Why was he funding scientists to research Corona viruses at UNC-Chapel Hill and those same scientists were transferred to the lab in Wuhan.  You all serve the same agenda.\n",
      "41 :  The answer to the motive is the communist Democrats in this country demanded that, the communist Democrats in this country need to be charged with treason.\n",
      "42 :  It may be possible that China manufactured coronavirus in the lab from bats. The guest says it was release intentionally. If so, I don&#39;t understand why China would release it on their own county first.\n",
      "43 :  NOT. TRUE. TRUMP WAS THE FIRST TO ACT BY STOPPING ALL FLIGHTS FROM CHINA IMMEDIATELY.\n",
      "44 :  This is not new information. It has already been stated by other Doctors, particularly Judy Mikovitz, that Fauci started the research in the U.S., but there was a moratorium on &quot;gain of function&quot; so Fauci funded the research in a Wuhan lab. But it&#39;s good to have her reinforce the TRUTH and bring it to the mainstream light! Of course she will be smeared and censored because they want to continue to push all aspects of this evil agenda.\n",
      "45 :  After the SARS outbreak, which went from bats, to trafficked endangered civet cats, to humans, public health experts begged China to close their wet markets, or at least stop the trafficking in endangered animals. If they had, the world would not have suffered from Covid-19, which went from bats, to trafficked endangered pangolins, to humans.\n",
      "46 :  Hmm, lab leak vs wet market origin??? Let&#39;s do some critical thinking...<br><br>It has been 17 months and there is NO proof provided of the wet market theory. If there was proof of the virus origin in the wet market then common sense would be that China would provide that evidence to dispel the lab leak theory. China has not provided a single BAT or piece of evidence of the wet market virus origin. <br><br>Over 3.5 million deaths due to the virus and the world does not know the origin of the virus and China has closed the investigation with NO transparency. I do not trust China as US Health officials have and do not want the US Taxpayer money going to ANY lab in the WORLD where the US scientist do not have FULL access. <br><br>Please let me know if you think the US should be funding experiments in countries where we do not have FULL access. <br><br>Please let me know if you have bee provided data or evidence of the lab leak theory or the wet market theory as the origin of the virus. I would love to hear thoughts.\n",
      "47 :  Why is China not be held accountable? A bio weapon released needs some questions answered!\n",
      "48 :  I&#39;m in day 5 of being positive. It freakin sucks. Cough, constant varying level of fever, and food taste like garbage. I hate the NIH for funding this crap and the CCP for releasing on the world. Fauci need to pay if we&#39;re not going to nuke Chinese cities. Yeah, I&#39;m pissed.\n",
      "49 :  China’s economy put out a positive gdp while the rest of the world is in life support in this pandemic, an accident? I think not. Who would’ve thought that trump’s trade war can lead to an unconventional warfare. So, stick around and see where else this goes. Maybe nuclear weapons next, thats 10000 years worth of quarantine from the fallout folks.\n"
     ]
    }
   ],
   "source": [
    "sample_comments = china_df['Comment'].sample(n=50, random_state=42)\n",
    "\n",
    "for i, comment in enumerate(sample_comments):\n",
    "    print(i, ': ', comment)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comment: The problem here is a damned if you do damned if you don&#39;t scenario. I have thought it was possible from the beginning that it was a manufactured virus, I have thought it was possible from the beginning that it was intentionally released. The first hurdle is proving if what she said is true from her research, and trusting that those vetting her research are not biased/influenced/or threatened with a counter agenda. The second hurdle, well, disproving her research does not mean the CCP were not negligible nor complicit, it just means her research does not prove it. But worst of all, what happens if she is right? Do we go to war? Lets say we just do sanctions, how is that justice? Lets say we want justice, that would mean war, and given the military and resources they have gained in the last half a century, how well will that turn out for the world? How much land, resources, politicians, celebrities do they own in the west? How much corrupted tech have they sold into use in the US Military? How much access do they have in our digital infrastructure? I think the best thing to do is ween ourselves and our allies off their manufacturing industry, immediately.\n",
      "Hate Speech Score: 0.044084347784519196\n",
      "---\n",
      "Comment: 83 dislikes come from Science denial Democrats or Chinese Communist Party officials.\n",
      "Hate Speech Score: 0.013863549567759037\n",
      "---\n",
      "Comment: Then why is there a patent registered to Bill Gates for this virus if it originated from China? Disinformation is real. Is necessary.\n",
      "Hate Speech Score: 0.00033295294269919395\n",
      "---\n",
      "Comment: ONE WORD : CHINA !\n",
      "Hate Speech Score: 0.2013436108827591\n",
      "---\n",
      "Comment: Rodricks A “Democrats pampering China”?🤣🤣🤣🤣\n",
      "Hate Speech Score: 0.006327620707452297\n",
      "---\n",
      "Comment: Multinational corporations love the Communist Party of China because they want access to that giant Chinese market. They don&#39;t care that the CCP is an evil regime. They accuse Trump of being what the Communist Party of China is. They&#39;re traitors! DE-COUPLE FROM COMMUNIST CHINA!\n",
      "Hate Speech Score: 0.49395111203193665\n",
      "---\n",
      "Comment: Remember the Chinese communist party to blame\n",
      "Hate Speech Score: 0.9939217567443848\n",
      "---\n",
      "Comment: We all kinda had a clue it was man made, We just didn&#39;t know where from... <br>Biden &amp; democrats has China under their Thumb.. Think about that..\n",
      "Hate Speech Score: 0.09570398926734924\n",
      "---\n",
      "Comment: Except the rest of the world has access to the genome of the virus too, and Tucker thinks we&#39;re all stupid enough to think the whole world is lying for China. I guess Fox viewers are this stupid.\n",
      "Hate Speech Score: 0.00031870914972387254\n",
      "---\n",
      "Comment: @@leomendez3195 not China, who gave the go ahead? Who wants to make vaccine mandatory?  Those leaders are to blame\n",
      "Hate Speech Score: 0.48383161425590515\n",
      "---\n",
      "Comment: Because she would give up her good salary, comfortable life, husband and family, prestigious position and other great things for  a US Citizenship.... Are you dumb? Also, the CCP will send someone after her. Is it all worth it for a citizenship? You are not the brightest apple on the tree...\n",
      "Hate Speech Score: 0.01561745721846819\n",
      "---\n",
      "Comment: Yes, and it is time that we send the Chinese Communist party the bill. They have to be hold accountable.\n",
      "Hate Speech Score: 0.9672717452049255\n",
      "---\n",
      "Comment: And China is licking their lips, the CCP is not the Chinese people but their government.\n",
      "Hate Speech Score: 0.25553077459335327\n",
      "---\n",
      "Comment: The Third World War has come, and the Chinese Communist Party has spread the virus to the world!💀☠👺😈👻👽👹☻👿👾🤖💩\n",
      "Hate Speech Score: 0.9691644906997681\n",
      "---\n",
      "Comment: 35,000,000 in tax dollars given to Wuhan China in 2009 by barckkk Hussein bin laden, then china gave dr Charles lieber 1,800,000 and designed a lab for him. I know probably just a clinsodense.\n",
      "Hate Speech Score: 0.8325598835945129\n",
      "---\n",
      "Comment: I&#39;m just wondering, what evidence does she have? If the virus was man made, why not make it more deadly? If China is the source, why would they let it spread in their own country?\n",
      "Hate Speech Score: 0.9360109567642212\n",
      "---\n",
      "Comment: @ Just enough This same network was running stories a few months ago about Gates having shady dealings with Epstein... Now they are proppin him up like a prophet... ID 2020 chipping everybody by 2021 that&#39;s his big thing, vaccines for population control, Globalist, ties to powerful people in China, he&#39;s a major contributor to the world health organization... He&#39;s no prophet he&#39;s a puppet master...\n",
      "Hate Speech Score: 0.6182632446289062\n",
      "---\n",
      "Comment: I prefer CCP Virus\n",
      "Hate Speech Score: 0.0001486453111283481\n",
      "---\n",
      "Comment: Bill Gates financed this chinese lab with millions of dollars through his Foundation  and spoke of this virus at an international event in 2015, even using images of it in his presentation! What more is there to say?!<br>Furthermore, Covid19 means Certificate of Vaccination Identification Artificial Inteligence (1=A; 9=I). Ghastly!!\n",
      "Hate Speech Score: 0.8734965324401855\n",
      "---\n",
      "Comment: This isnt new. Back in march a youtuber posted that the virus has 6 RNA inserts. Its man made. Problem is there are 2 labs in Wuhan. China and the NIH both had labs in Wuhan. Dr Fauchi has been pushing for gain of function of viruses. Obama had pulled the plug and Dr Fauchi still had it funded. Big money in vaccines. Who has profitted the most from Cov19?\n",
      "Hate Speech Score: 0.0385880172252655\n",
      "---\n",
      "Comment: Racist scientist blaming the Chinese!  Oh......wait a minute\n",
      "Hate Speech Score: 0.00013887758541386575\n",
      "---\n",
      "Comment: According to Judy Mikovitz the virus was made by a group along with anthony faucci who is responsible for it within wuhan lab\n",
      "Hate Speech Score: 0.9687874913215637\n",
      "---\n",
      "Comment: the democrats have been purchased by the CCP\n",
      "Hate Speech Score: 0.0001594546774867922\n",
      "---\n",
      "Comment: This explains why Wuhan P4 Lab is out of limit even for WHO. CCP has tentacles deep in scientific society -- quite a few American senior scientists were indicted for involvement in China&#39;s thousand-people plan. My deepest respect for Dr Limeng Yan for exposing this. She is a hero.\n",
      "Hate Speech Score: 0.028467271476984024\n",
      "---\n",
      "Comment: CHINESE BIO-WEAPON. MAN MADE GAIN OF FUNCTION CHIMERA VIRUS. DR. SHI ZHENGLI WUHAN P4 LAB. DR. FAUCI HAS THE RECEIPTS. CRIMES AGAINST HUMANITY. NUREMBERG TRIAL. GALLOWS!\n",
      "Hate Speech Score: 0.7021974325180054\n",
      "---\n",
      "Comment: CCP were lying with those numbers, here’s a nurse at Wuhan hospital tells it all <br><br><br>https://youtu.be/NVHFmVccWns\n",
      "Hate Speech Score: 0.007487427908927202\n",
      "---\n",
      "Comment: blame china\n",
      "Hate Speech Score: 0.019907942041754723\n",
      "---\n",
      "Comment: Sounds like Fauci and the NIH paid for part of it. They believed since they&#39;re restricted here they will fund a Lab where Frankenstein could be born without any regulation at all, especially unbeknownst to these idiots that if it served the CCP and its military it would especially be allowed.\n",
      "Hate Speech Score: 0.7725820541381836\n",
      "---\n",
      "Comment: It&#39;s just as likely this virus originated in a Chinese lab and if so it would be covered up with a handy meat market. I would bet lab techs frequently shop at meat markets and I wonder if they became ill before the spread of this virus.\n",
      "Hate Speech Score: 0.8982321619987488\n",
      "---\n",
      "Comment: Yes she is correct but before Wuhan it was developed at Fort Detrick and passed on to Wuhan labs. <br>George Webb has been saying this for some time already.<br>About four or five weeks ago all of his work, many years worth, was scrubbed from YouTube. You can no longer find him on YouTube under his own name but he can be found on other platforms.\n",
      "Hate Speech Score: 0.005258125253021717\n",
      "---\n",
      "Comment: They won&#39;t--just watch. Our corporate rulers benefit handsomely from war, and if they can&#39;t get one with Iran or Russia, they will have one with China. Other major benefit: the American public is becoming conditioned to give up their Constitutional rights based on a mix of propaganda and poorly executed science...or whenever the government says we &quot;are in danger.&quot;\n",
      "Hate Speech Score: 0.31770122051239014\n",
      "---\n",
      "Comment: we already know whaat wuhan lab was doing for the CDC aka Fauci.....this was known  a year ago....time for you and your shepherd MSNBC to catch up\n",
      "Hate Speech Score: 0.20960083603858948\n",
      "---\n",
      "Comment: I guess Puerto Ricans get runny noses too. Don&#39;t worry. Communism will that care of it.\n",
      "Hate Speech Score: 0.6416240334510803\n",
      "---\n",
      "Comment: His administration continues to attack us Americans!!! Communist president dictator! 🤬\n",
      "Hate Speech Score: 0.5555351376533508\n",
      "---\n",
      "Comment: Some countries have already tried to sue China but were unsuccessful\n",
      "Hate Speech Score: 0.0010041201021522284\n",
      "---\n",
      "Comment: Stop, you CCP troll. Here is 50 cents! GO AWAY!\n",
      "Hate Speech Score: 0.00013373870751820505\n",
      "---\n",
      "Comment: Everyone in South Korea and China wore masks and both countries disinfected streets and sidewalks.\n",
      "Hate Speech Score: 0.00017871262389235198\n",
      "---\n",
      "Comment: The answer is China is at fault.\n",
      "Hate Speech Score: 0.9982312321662903\n",
      "---\n",
      "Comment: She needs to be protected by China.\n",
      "Hate Speech Score: 0.9801832437515259\n",
      "---\n",
      "Comment: Imagine how brave this woman is, given WHERE she&#39;s from?! Love her!!! I wonder if she also knows that this particular virus began its journey at a University in North Carolina and was then outsourced to China during the Obama administration via Anthony Fauci, along with a cool $3.7 million to continue the &quot;chimeric construction&quot; to give the it &quot;gain of function&quot; (turbo-charge it)??\n",
      "Hate Speech Score: 0.015490952879190445\n",
      "---\n",
      "Comment: MSNBC you only speak to fools who do not bother to seek the truth. Bill Gates is 100% behind this pandemic and you hail him as some prophetic genius. Why was he funding scientists to research Corona viruses at UNC-Chapel Hill and those same scientists were transferred to the lab in Wuhan.  You all serve the same agenda.\n",
      "Hate Speech Score: 0.029169095680117607\n",
      "---\n",
      "Comment: The answer to the motive is the communist Democrats in this country demanded that, the communist Democrats in this country need to be charged with treason.\n",
      "Hate Speech Score: 0.3421958088874817\n",
      "---\n",
      "Comment: It may be possible that China manufactured coronavirus in the lab from bats. The guest says it was release intentionally. If so, I don&#39;t understand why China would release it on their own county first.\n",
      "Hate Speech Score: 0.8003130555152893\n",
      "---\n",
      "Comment: NOT. TRUE. TRUMP WAS THE FIRST TO ACT BY STOPPING ALL FLIGHTS FROM CHINA IMMEDIATELY.\n",
      "Hate Speech Score: 0.00032129930332303047\n",
      "---\n",
      "Comment: This is not new information. It has already been stated by other Doctors, particularly Judy Mikovitz, that Fauci started the research in the U.S., but there was a moratorium on &quot;gain of function&quot; so Fauci funded the research in a Wuhan lab. But it&#39;s good to have her reinforce the TRUTH and bring it to the mainstream light! Of course she will be smeared and censored because they want to continue to push all aspects of this evil agenda.\n",
      "Hate Speech Score: 0.5951623916625977\n",
      "---\n",
      "Comment: After the SARS outbreak, which went from bats, to trafficked endangered civet cats, to humans, public health experts begged China to close their wet markets, or at least stop the trafficking in endangered animals. If they had, the world would not have suffered from Covid-19, which went from bats, to trafficked endangered pangolins, to humans.\n",
      "Hate Speech Score: 0.7258496880531311\n",
      "---\n",
      "Comment: Hmm, lab leak vs wet market origin??? Let&#39;s do some critical thinking...<br><br>It has been 17 months and there is NO proof provided of the wet market theory. If there was proof of the virus origin in the wet market then common sense would be that China would provide that evidence to dispel the lab leak theory. China has not provided a single BAT or piece of evidence of the wet market virus origin. <br><br>Over 3.5 million deaths due to the virus and the world does not know the origin of the virus and China has closed the investigation with NO transparency. I do not trust China as US Health officials have and do not want the US Taxpayer money going to ANY lab in the WORLD where the US scientist do not have FULL access. <br><br>Please let me know if you think the US should be funding experiments in countries where we do not have FULL access. <br><br>Please let me know if you have bee provided data or evidence of the lab leak theory or the wet market theory as the origin of the virus. I would love to hear thoughts.\n",
      "Hate Speech Score: 0.08991342037916183\n",
      "---\n",
      "Comment: Why is China not be held accountable? A bio weapon released needs some questions answered!\n",
      "Hate Speech Score: 0.9882086515426636\n",
      "---\n",
      "Comment: I&#39;m in day 5 of being positive. It freakin sucks. Cough, constant varying level of fever, and food taste like garbage. I hate the NIH for funding this crap and the CCP for releasing on the world. Fauci need to pay if we&#39;re not going to nuke Chinese cities. Yeah, I&#39;m pissed.\n",
      "Hate Speech Score: 0.18743731081485748\n",
      "---\n",
      "Comment: China’s economy put out a positive gdp while the rest of the world is in life support in this pandemic, an accident? I think not. Who would’ve thought that trump’s trade war can lead to an unconventional warfare. So, stick around and see where else this goes. Maybe nuclear weapons next, thats 10000 years worth of quarantine from the fallout folks.\n",
      "Hate Speech Score: 0.0013252445496618748\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "sample_comments = china_df.sample(n=50, random_state=42)\n",
    "hate_scores = []\n",
    "for index, row in sample_comments.iterrows():\n",
    "    print(f\"Comment: {row['Comment']}\")\n",
    "    print(f\"Hate Speech Score: {row['Hate Speech Score']}\")\n",
    "    hate_scores.append(row['Hate Speech Score'])\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I manually look through all 50 sampled comments and label them, 1 means hate speech and 0 otherwise\n",
    "manual_labels = [1,0,0,1,0,1,1,0,1,1,1,1,0,1,0,0,0,1,1,0,0,0,0,1,1,1,1,1,1,0,0,0,1,1,0,1,0,1,0,0,1,1,0,0,1,1,1,1,1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "manual_labels = np.array(manual_labels)\n",
    "hate_scores = np.array(hate_scores)\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n",
    "\n",
    "fpr, tpr, roc_thresholds = roc_curve(manual_labels, hate_scores)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3421958088874817"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(manual_labels, hate_scores)\n",
    "# Calculate the distance to the top-left corner for each point\n",
    "distances = (fpr**2 + (1-tpr)**2)**0.5\n",
    "# Find the minimum distance and the corresponding optimal threshold\n",
    "optimal_idx = np.argmin(distances)\n",
    "optimal_threshold = thresholds[optimal_idx]\n",
    "optimal_threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence set a hate speech threshold to be 0.34 and add a new binary column \"Hate Speech\" based on \"Hate Speech Score\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bz/72zd2hjs5t5drbxxvfh0xhph0000gn/T/ipykernel_85457/2861758994.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  china_df['Hate Speech'] = china_df['Hate Speech Score'].apply(lambda score: 1 if score > optimal_threshold else 0)\n"
     ]
    }
   ],
   "source": [
    "china_df['Hate Speech'] = china_df['Hate Speech Score'].apply(lambda score: 1 if score > optimal_threshold else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comment PublishedAt</th>\n",
       "      <th>Comment</th>\n",
       "      <th>Video PublishedAt</th>\n",
       "      <th>Video Title</th>\n",
       "      <th>Channel</th>\n",
       "      <th>tokenized_text</th>\n",
       "      <th>normalized_tokens</th>\n",
       "      <th>reduced_tokens</th>\n",
       "      <th>Hate Speech Score</th>\n",
       "      <th>Hate Speech</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-03-16</td>\n",
       "      <td>Hey! First comment of 2023! Is it anniversary ...</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[Hey, First, comment, of, 2023, Is, it, annive...</td>\n",
       "      <td>[hey, comment, anniversary, day, china, virus]</td>\n",
       "      <td>[hey, comment, day, china, virus]</td>\n",
       "      <td>0.685360</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-03-26</td>\n",
       "      <td>Lora of good warnings here... that unfortunate...</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[Lora, of, good, warnings, here, that, unfortu...</td>\n",
       "      <td>[lora, good, warnings, unfortunately, true, sa...</td>\n",
       "      <td>[good, warnings, unfortunately, true, saying, ...</td>\n",
       "      <td>0.320128</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>The Chinese Coronavirus.</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[The, Chinese, Coronavirus]</td>\n",
       "      <td>[chinese, coronavirus]</td>\n",
       "      <td>[chinese, coronavirus]</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>I bet his favorite food is Chinese food. Gtfoh</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[I, bet, his, favorite, food, is, Chinese, foo...</td>\n",
       "      <td>[bet, favorite, food, chinese, food, gtfoh]</td>\n",
       "      <td>[bet, favorite, food, chinese, food, gtfoh]</td>\n",
       "      <td>0.999489</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-05-19</td>\n",
       "      <td>Did he just said that China is the most racist...</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Tucker: We must remain calm but not complacent...</td>\n",
       "      <td>Fox</td>\n",
       "      <td>[Did, he, just, said, that, China, is, the, mo...</td>\n",
       "      <td>[said, china, racist, country, world]</td>\n",
       "      <td>[said, china, racist, country, world]</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139445</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>American pandemic experts?        US 5 day iso...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[American, pandemic, experts, US, 5, day, isol...</td>\n",
       "      <td>[american, pandemic, experts, day, isolation, ...</td>\n",
       "      <td>[american, pandemic, experts, day, isolation, ...</td>\n",
       "      <td>0.001411</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139506</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>The great news as they will get natural immuni...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[The, great, news, as, they, will, get, natura...</td>\n",
       "      <td>[great, news, natural, immunity, won&amp;#39;t, ne...</td>\n",
       "      <td>[great, news, natural, immunity, need, communi...</td>\n",
       "      <td>0.710858</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139535</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Trump Trump Trump Trump Trump Trump and Cathol...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[Trump, Trump, Trump, Trump, Trump, Trump, and...</td>\n",
       "      <td>[trump, trump, trump, trump, trump, trump, cat...</td>\n",
       "      <td>[trump, trump, trump, trump, trump, trump, cat...</td>\n",
       "      <td>0.317390</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139540</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Wow that great news and Puerto Rico is so luck...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[Wow, that, great, news, and, Puerto, Rico, is...</td>\n",
       "      <td>[wow, great, news, puerto, rico, lucky, united...</td>\n",
       "      <td>[wow, great, news, puerto, rico, lucky, united...</td>\n",
       "      <td>0.698143</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>139579</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>That&amp;#39;s great news and watch how Puerto Ric...</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>Puerto Rico Covid Cases Soar Nearly 2800% In T...</td>\n",
       "      <td>MSNBC</td>\n",
       "      <td>[That&amp;#39;s, great, news, and, watch, how, Pue...</td>\n",
       "      <td>[that&amp;#39;s, great, news, watch, puerto, rico,...</td>\n",
       "      <td>[great, news, watch, puerto, rico, thrives, mo...</td>\n",
       "      <td>0.939829</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10901 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Comment PublishedAt                                            Comment  \\\n",
       "2               2023-03-16  Hey! First comment of 2023! Is it anniversary ...   \n",
       "9               2021-03-26  Lora of good warnings here... that unfortunate...   \n",
       "18              2020-06-22                           The Chinese Coronavirus.   \n",
       "21              2020-06-09     I bet his favorite food is Chinese food. Gtfoh   \n",
       "23              2020-05-19  Did he just said that China is the most racist...   \n",
       "...                    ...                                                ...   \n",
       "139445          2021-12-28  American pandemic experts?        US 5 day iso...   \n",
       "139506          2021-12-28  The great news as they will get natural immuni...   \n",
       "139535          2021-12-28  Trump Trump Trump Trump Trump Trump and Cathol...   \n",
       "139540          2021-12-28  Wow that great news and Puerto Rico is so luck...   \n",
       "139579          2021-12-28  That&#39;s great news and watch how Puerto Ric...   \n",
       "\n",
       "       Video PublishedAt                                        Video Title  \\\n",
       "2             2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "9             2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "18            2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "21            2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "23            2020-03-09  Tucker: We must remain calm but not complacent...   \n",
       "...                  ...                                                ...   \n",
       "139445        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139506        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139535        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139540        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "139579        2021-12-28  Puerto Rico Covid Cases Soar Nearly 2800% In T...   \n",
       "\n",
       "       Channel                                     tokenized_text  \\\n",
       "2          Fox  [Hey, First, comment, of, 2023, Is, it, annive...   \n",
       "9          Fox  [Lora, of, good, warnings, here, that, unfortu...   \n",
       "18         Fox                        [The, Chinese, Coronavirus]   \n",
       "21         Fox  [I, bet, his, favorite, food, is, Chinese, foo...   \n",
       "23         Fox  [Did, he, just, said, that, China, is, the, mo...   \n",
       "...        ...                                                ...   \n",
       "139445   MSNBC  [American, pandemic, experts, US, 5, day, isol...   \n",
       "139506   MSNBC  [The, great, news, as, they, will, get, natura...   \n",
       "139535   MSNBC  [Trump, Trump, Trump, Trump, Trump, Trump, and...   \n",
       "139540   MSNBC  [Wow, that, great, news, and, Puerto, Rico, is...   \n",
       "139579   MSNBC  [That&#39;s, great, news, and, watch, how, Pue...   \n",
       "\n",
       "                                        normalized_tokens  \\\n",
       "2          [hey, comment, anniversary, day, china, virus]   \n",
       "9       [lora, good, warnings, unfortunately, true, sa...   \n",
       "18                                 [chinese, coronavirus]   \n",
       "21            [bet, favorite, food, chinese, food, gtfoh]   \n",
       "23                  [said, china, racist, country, world]   \n",
       "...                                                   ...   \n",
       "139445  [american, pandemic, experts, day, isolation, ...   \n",
       "139506  [great, news, natural, immunity, won&#39;t, ne...   \n",
       "139535  [trump, trump, trump, trump, trump, trump, cat...   \n",
       "139540  [wow, great, news, puerto, rico, lucky, united...   \n",
       "139579  [that&#39;s, great, news, watch, puerto, rico,...   \n",
       "\n",
       "                                           reduced_tokens  Hate Speech Score  \\\n",
       "2                       [hey, comment, day, china, virus]           0.685360   \n",
       "9       [good, warnings, unfortunately, true, saying, ...           0.320128   \n",
       "18                                 [chinese, coronavirus]           0.000340   \n",
       "21            [bet, favorite, food, chinese, food, gtfoh]           0.999489   \n",
       "23                  [said, china, racist, country, world]           0.000678   \n",
       "...                                                   ...                ...   \n",
       "139445  [american, pandemic, experts, day, isolation, ...           0.001411   \n",
       "139506  [great, news, natural, immunity, need, communi...           0.710858   \n",
       "139535  [trump, trump, trump, trump, trump, trump, cat...           0.317390   \n",
       "139540  [wow, great, news, puerto, rico, lucky, united...           0.698143   \n",
       "139579  [great, news, watch, puerto, rico, thrives, mo...           0.939829   \n",
       "\n",
       "        Hate Speech  \n",
       "2                 1  \n",
       "9                 0  \n",
       "18                0  \n",
       "21                1  \n",
       "23                0  \n",
       "...             ...  \n",
       "139445            0  \n",
       "139506            1  \n",
       "139535            0  \n",
       "139540            1  \n",
       "139579            1  \n",
       "\n",
       "[10901 rows x 10 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4774"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_df['Hate Speech'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hate Speech Score - Hate Intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10901.000000\n",
       "mean         0.396057\n",
       "std          0.403771\n",
       "min          0.000131\n",
       "25%          0.008723\n",
       "50%          0.203034\n",
       "75%          0.861640\n",
       "max          0.999800\n",
       "Name: Hate Speech Score, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_df['Hate Speech Score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(china_df['Hate Speech Score'], vert=False)\n",
    "plt.title('Horizontal Box Plot of Hate Speech Score')\n",
    "plt.xlabel('Hate Speech Score')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Hate Score Distribution'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "china_df['Hate Speech Score'].plot(kind='hist', edgecolor='black', title='Hate Score Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comment PublishedAt           object\n",
       "Comment                       object\n",
       "Video PublishedAt      datetime64[s]\n",
       "Video Title                   object\n",
       "Channel                       object\n",
       "tokenized_text                object\n",
       "normalized_tokens             object\n",
       "reduced_tokens                object\n",
       "Hate Speech Score            float64\n",
       "Hate Speech                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Average Hate Speech Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01</td>\n",
       "      <td>0.441886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>0.443404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>0.386435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>0.322001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>0.383086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>0.475127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>0.372409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-08</td>\n",
       "      <td>0.365997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-09</td>\n",
       "      <td>0.417827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-10</td>\n",
       "      <td>0.290872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-11</td>\n",
       "      <td>0.367612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-12</td>\n",
       "      <td>0.327474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-01</td>\n",
       "      <td>0.298273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-02</td>\n",
       "      <td>0.224269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-03</td>\n",
       "      <td>0.278728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>0.345521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2021-05</td>\n",
       "      <td>0.357973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2021-06</td>\n",
       "      <td>0.344697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2021-07</td>\n",
       "      <td>0.271475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021-08</td>\n",
       "      <td>0.353240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-09</td>\n",
       "      <td>0.343279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021-10</td>\n",
       "      <td>0.392170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2021-11</td>\n",
       "      <td>0.410242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>0.405912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Month  Average Hate Speech Score\n",
       "0   2020-01                   0.441886\n",
       "1   2020-02                   0.443404\n",
       "2   2020-03                   0.386435\n",
       "3   2020-04                   0.322001\n",
       "4   2020-05                   0.383086\n",
       "5   2020-06                   0.475127\n",
       "6   2020-07                   0.372409\n",
       "7   2020-08                   0.365997\n",
       "8   2020-09                   0.417827\n",
       "9   2020-10                   0.290872\n",
       "10  2020-11                   0.367612\n",
       "11  2020-12                   0.327474\n",
       "12  2021-01                   0.298273\n",
       "13  2021-02                   0.224269\n",
       "14  2021-03                   0.278728\n",
       "15  2021-04                   0.345521\n",
       "16  2021-05                   0.357973\n",
       "17  2021-06                   0.344697\n",
       "18  2021-07                   0.271475\n",
       "19  2021-08                   0.353240\n",
       "20  2021-09                   0.343279\n",
       "21  2021-10                   0.392170\n",
       "22  2021-11                   0.410242\n",
       "23  2021-12                   0.405912"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_hate_speech_scores = china_df.groupby(pd.Grouper(key='Video PublishedAt', freq='M'))['Hate Speech Score'].mean().reset_index()\n",
    "monthly_hate_speech_scores['Video PublishedAt'] = monthly_hate_speech_scores['Video PublishedAt'].dt.strftime('%Y-%m')\n",
    "\n",
    "# Rename the columns for clarity\n",
    "monthly_hate_speech_scores.columns = ['Month', 'Average Hate Speech Score']\n",
    "monthly_hate_speech_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer supported. Convert to a numpy array before indexing instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [84]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m5\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmonthly_hate_speech_scores\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mMonth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmonthly_hate_speech_scores\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAverage Hate Speech Score\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAverage Hate Speech Score Over 2020 and 2021\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMonth\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/pyplot.py:2757\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2755\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   2756\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;241m*\u001b[39margs, scalex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, scaley\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2757\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2758\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2759\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_axes.py:1632\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1391\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1392\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1629\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1630\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1631\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1632\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1633\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1634\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    310\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    311\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py:487\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m    484\u001b[0m         kw[prop_name] \u001b[38;5;241m=\u001b[39m val\n\u001b[1;32m    486\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(xy) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m--> 487\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[43m_check_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxy\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    488\u001b[0m     y \u001b[38;5;241m=\u001b[39m _check_1d(xy[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m    489\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/cbook/__init__.py:1327\u001b[0m, in \u001b[0;36m_check_1d\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1321\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings(record\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m w:\n\u001b[1;32m   1322\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\n\u001b[1;32m   1323\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malways\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1324\u001b[0m         category\u001b[38;5;241m=\u001b[39m\u001b[38;5;167;01mWarning\u001b[39;00m,\n\u001b[1;32m   1325\u001b[0m         message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSupport for multi-dimensional indexing\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1327\u001b[0m     ndim \u001b[38;5;241m=\u001b[39m \u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mndim\n\u001b[1;32m   1328\u001b[0m     \u001b[38;5;66;03m# we have definitely hit a pandas index or series object\u001b[39;00m\n\u001b[1;32m   1329\u001b[0m     \u001b[38;5;66;03m# cast to a numpy array.\u001b[39;00m\n\u001b[1;32m   1330\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(w) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/series.py:1144\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1141\u001b[0m     key \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(key, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m)\n\u001b[1;32m   1142\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_rows_with_mask(key)\n\u001b[0;32m-> 1144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_with\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/series.py:1154\u001b[0m, in \u001b[0;36mSeries._get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1149\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   1150\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndexing a Series with DataFrame is not \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1151\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msupported, use the appropriate DataFrame column\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1152\u001b[0m     )\n\u001b[1;32m   1153\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m-> 1154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_values_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1156\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_list_like(key):\n\u001b[1;32m   1157\u001b[0m     \u001b[38;5;66;03m# e.g. scalars that aren't recognized by lib.is_scalar, GH#32684\u001b[39;00m\n\u001b[1;32m   1158\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc[key]\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/series.py:1194\u001b[0m, in \u001b[0;36mSeries._get_values_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39many_none(\u001b[38;5;241m*\u001b[39mkey):\n\u001b[1;32m   1190\u001b[0m     \u001b[38;5;66;03m# mpl compat if we look up e.g. ser[:, np.newaxis];\u001b[39;00m\n\u001b[1;32m   1191\u001b[0m     \u001b[38;5;66;03m#  see tests.series.timeseries.test_mpl_compat_hack\u001b[39;00m\n\u001b[1;32m   1192\u001b[0m     \u001b[38;5;66;03m# the asarray is needed to avoid returning a 2D DatetimeArray\u001b[39;00m\n\u001b[1;32m   1193\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key])\n\u001b[0;32m-> 1194\u001b[0m     \u001b[43mdisallow_ndim_indexing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1195\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m   1197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, MultiIndex):\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexers/utils.py:341\u001b[0m, in \u001b[0;36mdisallow_ndim_indexing\u001b[0;34m(result)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;124;03mHelper function to disallow multi-dimensional indexing on 1D Series/Index.\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    338\u001b[0m \u001b[38;5;124;03min GH#30588.\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mndim(result) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 341\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    342\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMulti-dimensional indexing (e.g. `obj[:, None]`) is no longer \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    343\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msupported. Convert to a numpy array before indexing instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    344\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer supported. Convert to a numpy array before indexing instead."
     ]
    },
    {
     "data": {
      "image/png": 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l21OzTMfW0j7qh9NuoX2pqncnuSXJoe7+2Wmajd0tsn9rSW7fDK1zk1xZVSe7+1unZUJeyqJ/dz7b3b9K8ququifJpUnE1pm1yN5dl+TveuNFQMeq6idJLkny76dnRF6GPTXL9NOIPupnde26d1V1YZI7knzSv6bPOrvuX3df1N1v7+63J/mHJH8jtM4ai/zd+e0kH6yq/VX1miTvT/LYaZ6T37bI3j2ZjTuSqaq3ZuMDjp84rVOyV3tqltE7W+2jflbWgnv3uSRvTvLVzbsjJ9sHrJ4VFtw/zlKL7F93P1ZV30vyUJIXktzS3Tv+d3VOnwX/7H0hyW1V9XA2npa6sbufPWND8xtV9Y0klyc5t6qOJ/l8klclL69ZvIM8AMAg7yAPADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMCg/wWw2xl1y4WfzAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(monthly_hate_speech_scores['Month'], monthly_hate_speech_scores['Average Hate Speech Score'], marker='o')\n",
    "plt.title('Average Hate Speech Score Over 2020 and 2021')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Average Hate Speech Score')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}