The majority of existing work on text clustering has focused on topic-based clustering, where high accuracies can be achieved even for datasets with a large number of classes (e.g., 20 Newsgroups).
there has been relatively little work on sentiment-based clustering and the related task of unsupervised polarity classification, where the goal is to cluster (or classify) a set of documents (e.g., reviews) according to the polarity (e.g., "thumbs up" or "thumbs down") expressed by the author in an unsupervised manner.