How can I apply naive Bayes classifier for three classes (Positive, Negative and Neutral) in text data? I found a naive Bayes classifier for positive sentiment or a negative sentiment Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets. But with most available datasets online, sentiments are classified into 3 types: positive, negative, and neutral.
How does the naive Bayes formula change for such cases? Or does it remain the same, and we only consider the positive and negative to calculate the log likelihoods-likelihoods?
Use TextBlob to get the text sentiment polarity and subjectivity. polarity is negative or positive sentiment and subjectivity is how factual the sentiment is.
my_valance=TextBlob(sentence)
df.loc[key,'sentiment_polarity']=my_valance.sentiment.polarity
df.loc[key,'sentiment_subjectivity']=my_valance.sentiment.subjectivity