Dealing with diverse text data
I'm not sure how you are applying a regression framework for document classification. The way I'd approach the problem is to apply a standard discriminative classification approach such as SVM.

In a discriminative classification approach the notion of similarity or inverse distance between data points (documents in this case) is pivotal. Fortunately for documents, there is a standard way of defining pairwise similarity. This is the standard cosine similarity measure which makes use of document length normalization to take different document lengths into account.

Thus, practically speaking, in cosine similarity you would work with relative term weights normalized by document lengths and hence document length diversity should not be a major issue in the similarity computation.

One also has to be careful when applying idf in term weights. If the number of documents is not significantly large the idf measure may be statistically imprecise thus adding noise to the term weights. It's also a standard practice to ignore stopwords and punctuations.