How to implement TF_IDF feature weighting with Naive Bayes

Question

I'm trying to implement the naive Bayes classifier for sentiment analysis. I plan to use the TF-IDF weighting measure. I'm just a little stuck now. NB generally uses the word(feature) frequency to find the maximum likelihood. So how do I introduce the TF-IDF weighting measure in naive Bayes?

Cooperative

well were you able to find out the way as to how this can be done, since, I am too stuck with the same problem. I am trying to search about the same but getting nothing definite. – Johnettajohnette

Answer 1

You use the TF-IDF weights as features/predictors in your statistical model. I suggest to use either gensim [1]or scikit-learn [2] to compute the weights, which you then pass to your Naive Bayes fitting procedure.

The scikit-learn 'working with text' tutorial [3] might also be of interest.

[1] http://scikit-learn.org/dev/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html

[2] http://radimrehurek.com/gensim/models/tfidfmodel.html

[3] http://scikit-learn.github.io/scikit-learn-tutorial/working_with_text_data.html

Rattlebrained

Updated link to point 3 - scikit-learn.org/stable/tutorial/text_analytics/… – Samphire