Probabilistic topic models

This tutorial describes latent Dirichlet allocation (LDA), one of the simplest topic models, and then describes a variety of ways that the authors can build on it, which include dynamic topic models, correlated topic models, supervised topic models, author-topic models, bursty topic models, Bayesian nonparametric topic models, and others.

Abstract

Probabilistic topic modeling provides a suite of tools for the unsupervised analysis of large collections of documents. Topic modeling algorithms can uncover the underlying themes of a collection and decompose its documents according to those themes. This analysis can be used for corpus exploration, document search, and a variety of prediction problems.