Probabilistic Topic Models for Text Data Retrieval and Analysis

Text data include all kinds of natural language text such as web pages, news articles, scientific literature, emails, enterprise documents, and social media posts. 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 many extensions of them, 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 will systematically review the major research progress in probabilistic topic models and discuss their applications in text retrieval and text mining. The tutorial will provide (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) a broad overview of all the major representative topic models (that are usually extensions of PLSA or LDA), and (3) a discussion of major challenges and future research directions. The tutorial should be appealing to anyone who would like to learn about topic models, how and why they work, their widespread applications, and the remaining research challenges to be solved, including especially graduate students, researchers who want to develop new topic models, and practitioners who want to apply topic models to solve many application problems. The attendants are expected to have basic knowledge of probability and statistics.