feature importance between two data distributions I have two textual datasets collected from different domains (Twitter and Reddit). 

I extracted a set of features in the same way from these two datasets, one of these features as an example called X_positive.

How could I know if the feature X_positive is more representative (exists more) in one of the datasets?
My two cents:
I can think of several ways calculating specific terms repressiveness:

1) Calculating rate of documents(tweets/posts) where these terms appear more than 
1(or X > 1) times. You can decide on X by looking at the distribution of frequency per document.
2) Calculating terms TF-IDF score per each document and aggregating across all documents. Aggregating across all document  depends on your need and what you mean by representative.
3) Calculating terms appearance rate - how many times these terms appear divided by total number of words in the corpus(possibly after subtracting stop words).