# Identifying most informative (sub)words/vectors that help classify a sample

I am classifying text using fastText which is a word2vec library that can also create vectors for character level n-grams and I have successfully trained a binary classifier.

Now I’d like to see what words or subword n-grams are the most predictive of a class for the two classes (e.g. if classifier sees a word forest or a subword res then that might be a strong indication that the document has label Nature, but if it sees word “and” then that is probably not very informative for this classification task).

Therefore, I guess the question could be phrased as:

Given vectors representing words and subwords and a trained fastText classifier, what would be the best way to get a list of e.g. top 10 most informative words and subwords for deciding which class a sample belongs to?

Even though I’d be glad if you could make specific suggestions that consider my current setup with fastText, I’m also open to the more general solution suggestions.

Thanks