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 datafi). 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 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) 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.