# 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?

• post a sample of the data your trying to classify. Will it have one output label or multiple? Are you using CountVectorizer or TfIDF to tokenize your words? – Golden Lion Feb 15 at 22:35
• naive bayes is a classifer combined in a pipeline with countvectorize in the preprocessing part of the pipeline. – Golden Lion Mar 8 at 12:47

my_valance=TextBlob(sentence)