Evaluating a linear SVM on an NLP corpus where there are 150,000 data examples but each language sample is reasonably short(10-15 words). This is evaluated against a code that is a topic. For example "trucking water" -> "8215". The distribution is very skewed t the top 20 codes out of 411. Looking for best practices on feature engineering and sampling.