It is said that to achieve a good result (many different metrics) for text classification, it is not always a business of selecting the algorithm/classifier. Sometimes, it is even more important to find a good feature selection method given the specific task.
However, even before feature selection, selection of the category set actually will determine the final result a lot. Let's say, in many cases, the categories are actually semi-flexible or totally flexible.
I can imagine, to get a better set of categories, both the purpose of text classification and the statistical feature of the text play important roles.
I want to know, is there any tutorial/discussion/suggestion on how to select the categories? And what methods can be applied to analyse the statistical features of the text collection? (I can think of using clustering.)