In one paper on ML I read that chi square distribution is used to reduce the number of features. In that paper, features are words. That paper is related to Sentiment Analysis, so we have "positive", "negative" and "neutral" category.
How to calculate chi square distribution in that case?
In Python there is
scipy.stats.chisquarewhich gives chi_square value and a p_value. What do we do then with these two pieces of information?
What to do for example with word "good" as a feature?
How to calculate chi square distribution, and what to do with that?
What does it mean to exclude some feature from the set of features, because in that paper it is mentioned that we take n of them with top chi square.
I really don't know how to do it. If there is any paper or book or link to learn that, please tell me.