On March 10 the famous Silicon Bank crashed causing much reaction. Some were polarizing, and some were not so much. Understanding sentiment analysis allows machine learning to predict what emotion was displayed in the text. The emotions consisted of positive, negative, and neutral.
The dataset incorporates info from X(formerly Twitter)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import nltk
from nltk.corpus import stopwords
nltk.download("all")
import seaborn as sns
from nltk.tokenize import RegexpTokenizer, word_tokenize
from nltk.corpus import stopwords
import string
from nltk import WordPunctTokenizer
nltk.download('punkt')
nltk.download('stopwords')
stopwords.words("english")