automatic feature selection
Frequency delta is not necessary a sound method. A better approach would be to use a statistical test for independence, like chi square independence test which can tell you if two categorical samples does not come from the same distribution. A similar construction can be done with G statistic. See more on wikipedia.

One problem you will certainly encounter is that some categorical distribution for ngrams have a large number of categories and the test is heavily applicable. To avoid that you can group the frequencies which are very small in both distributions into a single category: other. That way you limit the number of categories but comparison remains significant.

Simply comparing differences is not enough since you do not have some threshold to use which have also a meaning, while a statistical test produces p-values which are probabilities in the end. 

On the other hand, you should consider the following issue with both approaches (the one you proposed and the one I presented you): they test the individual contribution of one feature, independent of others. As such, they will not catch situations where two features have insignificant contribution, but together they have a significant one.