I have a set of English and foreign language documents that I would to perform k-means clustering on to find document groups by topic. These documents are concatenated social media comments for individual users, which may be 1000-5000 words. (For example, a document may contain every question user 'Paul' has asked on StackExchange.)
I have performed feature extraction in two ways:
(1) extracting entity pronouns from a known list, which proved to have too poor coverage on the document set
(2) raw tokenisation, which proved too noisy with my poor quality feature selection
My usual strategy for feature selection for supervised learning is to calculate TF/IDF scores and systematically try different thresholds to provide the best cross validation performance. (Clearly impossible here.)
I have TF/IDF scores and I've tried using intuition to select a good threshold, but I'm struggling to evaluate if the clustering is good or bad. Google has returned some papers but nothing that is as prescriptive as I would like.
Any suggestions on getting started with feature selection for k-means or other unsupervised clustering?