ML_MAJOR_PROJECT_Sentiment_Analysis_Reddit_Data.ipynb

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  "metadata": {
    "colab": {
      "name": "ML_MAJOR_PROJECT_Sentiment_Analysis_Reddit_Data.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AniketMishra23/ML-MAY-Sentiment-Analysis/blob/main/ML_MAJOR_PROJECT_Sentiment_Analysis_Reddit_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ft_EIOr9M8d"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjlZydHF-aCa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "05f6ffb3-3263-43a3-dbb9-0fdb8019e959"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/ML MAY BATCH MAJOR PROJECT/Reddit_Data.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>clean_comment</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>family mormon have never tried explain them t...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>buddhism has very much lot compatible with chr...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>seriously don say thing first all they won get...</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>what you have learned yours and only yours wha...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>for your own benefit you may want read living ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       clean_comment  category\n",
              "0   family mormon have never tried explain them t...         1\n",
              "1  buddhism has very much lot compatible with chr...         1\n",
              "2  seriously don say thing first all they won get...        -1\n",
              "3  what you have learned yours and only yours wha...         0\n",
              "4  for your own benefit you may want read living ...         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhbO2mcTTvFV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce51fe50-3fd3-4cb0-a70a-fc3778a06111"
      },
      "source": [
        "df['category'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 1    15830\n",
              " 0    13142\n",
              "-1     8277\n",
              "Name: category, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p21rNy6mh_7M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "798d47a8-f2c3-4bbf-de3f-bcf90b99b731"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 37249 entries, 0 to 37248\n",
            "Data columns (total 2 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   clean_comment  37149 non-null  object\n",
            " 1   category       37249 non-null  int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 582.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "syAi8XmZqVjb",
        "outputId": "845d188d-0b12-4b04-82ea-d865fe2b2b78"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clean_comment    100\n",
              "category           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3wEgahQyqgDP",
        "outputId": "9ae61415-18dc-4589-bbc1-e6f982396612"
      },
      "source": [
        "df.dropna(axis = 0, inplace = True)\n",
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clean_comment    0\n",
              "category         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9FhhondJVIYm"
      },
      "source": [
        "x = df['clean_comment'].values\n",
        "y = df['category'].astype(str).values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00-LM2LgVcR0"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=103)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VX2Q1MVuVkRU"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uqkax_OVV27d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e4bf36d-02f6-499e-ce2f-d92e76b81836"
      },
      "source": [
        "vect = CountVectorizer(stop_words='english')\n",
        "vect"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
              "                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
              "                lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
              "                ngram_range=(1, 1), preprocessor=None, stop_words='english',\n",
              "                strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
              "                tokenizer=None, vocabulary=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBrpv5TiWbpN"
      },
      "source": [
        "x_train_vect = vect.fit_transform(x_train.astype(\"U\"))\n",
        "x_test_vect = vect.transform(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jC_OS0eqbTJo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c048155-3ef4-40cb-dc5a-3eef65e52021"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC()\n",
        "model.fit(x_train_vect,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhzpRewJsnwo"
      },
      "source": [
        "y_pred = model.predict(x_test_vect)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5Vbre99qqvC"
      },
      "source": [
        "from sklearn.metrics import accuracy_score,classification_report"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ypn325gKN5u4",
        "outputId": "d34de0c1-422b-415b-9150-ddecdfa6fd82"
      },
      "source": [
        "accuracy_score(y_pred,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.776043068640646"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "giU2jOBDszBY",
        "outputId": "174fcf8a-7017-44a1-fe0f-4624e4a1e766"
      },
      "source": [
        "print(classification_report(y_pred,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.49      0.86      0.62      1450\n",
            "           0       0.95      0.72      0.82      5153\n",
            "           1       0.78      0.81      0.80      4542\n",
            "\n",
            "    accuracy                           0.78     11145\n",
            "   macro avg       0.74      0.80      0.75     11145\n",
            "weighted avg       0.82      0.78      0.79     11145\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkRV73jos2Lu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c6377f4b-f8ce-43c5-9a54-53ded3898576"
      },
      "source": [
        "test = df['clean_comment'][658]\n",
        "test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'congress delhi please and vote delhi pehle aap '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T55mybCis5lQ",
        "outputId": "4ea70584-97d5-4ad0-f349-3832e8e8a751"
      },
      "source": [
        "df['category'][658]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrLkNsANtCgi",
        "outputId": "ff20f311-4a90-406f-99ca-b8354c050e3e"
      },
      "source": [
        "test = vect.transform([df['clean_comment'][658]])\n",
        "model.predict(test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZTyhEw0tUrB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7a7fc34-c947-4b78-80fe-9d1a3936833a"
      },
      "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "text_model = make_pipeline(CountVectorizer(),SVC())\n",
        "\n",
        "text_model.fit(x_train,y_train)\n",
        "\n",
        "y_pred = text_model.predict(x_test)\n",
        "y_pred\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '0', '1', ..., '-1', '1', '0'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJd3CH1AcHqJ",
        "outputId": "10675b3f-9348-4ec5-d787-5eda7952fc25"
      },
      "source": [
        "text_model.predict([df['clean_comment'][0]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RV07J7qIcPl_",
        "outputId": "034ce985-8e02-4ce4-d9ec-91ec8cd89149"
      },
      "source": [
        "text_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('countvectorizer',\n",
              "                 CountVectorizer(analyzer='word', binary=False,\n",
              "                                 decode_error='strict',\n",
              "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
              "                                 input='content', lowercase=True, max_df=1.0,\n",
              "                                 max_features=None, min_df=1,\n",
              "                                 ngram_range=(1, 1), preprocessor=None,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
              "                                 tokenizer=None, vocabulary=None)),\n",
              "                ('svc',\n",
              "                 SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None,\n",
              "                     coef0=0.0, decision_function_shape='ovr', degree=3,\n",
              "                     gamma='scale', kernel='rbf', max_iter=-1,\n",
              "                     probability=False, random_state=None, shrinking=True,\n",
              "                     tol=0.001, verbose=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fLQLeVePTFW",
        "outputId": "d8ce3e0c-038c-43ff-9a3a-aefe991e6558"
      },
      "source": [
        "import joblib\n",
        "joblib.dump(text_model,'Sentiment-Analysis')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sentiment-Analysis']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QG6Mivmyv1fl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}