Text mining and topic models

Classifiers for documents are useful for many applications. Major uses for binary classifiers include spam detection and personalization of streams of news articles. Multiclass classifiers are useful for routing messages to recipients. Most classifiers for documents are designed to categorize according to subject matter. However, it is also possible to learn to categorize according to qualitative criteria such as helpfulness for product reviews submitted by consumers. In many applications of multiclass classification, a single document can belong to more than one category, so it is correct to predict more than one label. This task is specifically called multilabel classification. In standard multiclass classification, the classes are mutually exclusive, i.e. a special type of negative correlation is fixed in advance. In multilabel classification, it is important to learn the positive and negative correlations between classes.