# Did my text data come from two distinct distributions?

I have labeled text data from two different classes. I have calculated tfidf feature representation of all the sentences in question. I have a huge matrix where rows are sentences and columns are tfidf scores for each word in them. Using these features I have implemented a random forest classifier to classify the two classes and achieved reasonable accuracy. But the initial hypothesis that I started with was: Is there some n-dimensional space where my text data separate out? Or do the sentences belonging to each class cluster in some n-dimensional space? Can you suggest some way of doing that?

I thought of it this way: I will take two feature vectors at random and calculate the pairwise similarity. The pairs will be from either $$(+1,+1)$$,$$(-1,-1)$$, or $$(+1,-1)$$. (Given I have two classes $$+1$$ and $$-1$$). Repeat this multiple times. Now I will get three distributions. From that, we will be able to say how much $$+1$$ is different from $$-1$$.