A Tutorial on Probabilistic Topic Models for Text Data Retrieval and Analysis

Abstract

As text data continues to grow quickly, it is increasingly important to develop intelligent systems to help people manage and make use of vast amounts of text data (big text data''). As a new family of effective general approaches to text data retrieval and analysis, probabilistic topic models---notably Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocations (LDA), and their many extensions---have been studied actively in the past decade with widespread applications. These topic models are powerful tools for extracting and analyzing latent topics contained in text data; they also provide a general and robust latent semantic representation of text data, thus improving many applications in information retrieval and text mining. Since they are general and robust, they can be applied to text data in any natural language and about any topics. This tutorial systematically reviews the major research progress in probabilistic topic models and discuss their applications in text retrieval and text mining. The tutorial provides (1) an in-depth explanation of the basic concepts, underlying principles, and the two basic topic models (i.e., PLSA and LDA) that have widespread applications, (2) an introduction to EM algorithms and Bayesian inference algorithms for topic models, (3) a hands-on exercise to allow the tutorial attendants to learn how to use the topic models implemented in the MeTA Open Source Toolkit and experiment with provided data sets, (4) a broad overview of all the major representative topic models that extend PLSA or LDA, and (5) a discussion of major challenges and future research directions.