I am currently having a data set, class 1 with about 8000 short text files and class 2 with about 3000 short text files. I applied LibSVM and tried a couple of parameter combinations in the cross-validation experiment.
Generally the class 1 precision falls into the range of (85%, 90%); the class 2 precision falls into the range of (70% , 75%); the recall of both class 1 and class 2 fall into the range of (80% , 85%).
For the text classification purposes, I built text feature space following the common approaches, tokening the document, filtering the stopwords and building the word vector using tf-idf or binary frequency, etc. I also tried n-gram model to build the feature space. But these approaches did not improve the performance a lot. I would like to know are there any other ways that may help tune the LibSVM to improve the performance. LibSVM provides grid search for parameter setting up, but it runs pretty slow.