Increase F1 score on a text corpus I'm doing some classification with a text corpus [professional emails], I've already done all the basic
pre processing steps (stemming, remove stop words, punctuation, low frequency words, words length ...)  which gives me a F1-score = 0.6.

I was wondering what could be the next step(s) to improve my results, I thought of "stacking" (use multiple classifiers) and maybe removing words which are common to the different classes (4 differents types of texts) [ not sure about this solution ]

Edit:

I tried with 15 differents classifier the best one is gradient boosting (gbm )with an F1-score=0.6002159.
The features have been extracted through a bag of words ( Document Term Matrix ) with Tf-Idf [I don't think taking into account the length of a text, punctuation or others is relevant] - [It is mainly professional emails]
Have you tried without the Tf-Idf weighting? What about a bi-gram analysis?

You can also review the pre processing, maybe without removing the low frequency words (wich the tf idf already does). Another idea is to remove one very high frequency word that appear on your corpora.