Topic evolution based on the probabilistic topic model: a review

Abstract

Accurately representing the quantity and characteristics of users’ interest in certain topics is an important problem facing topic evolution researchers, particularly as it applies to modern online environments. Search engines can provide information retrieval for a specified topic from archived data, but fail to reflect changes in interest toward the topic over time in a structured way. This paper reviews notable research on topic evolution based on the probabilistic topic model from multiple aspects over the past decade. First, we introduce notations, terminology, and the basic topic model explored in the survey, then we summarize three categories of topic evolution based on the probabilistic topic model: the discrete time topic evolution model, the continuous time topic evolutionmodel, and the online topic evolution model. Next, we describe applications of the topic evolution model and attempt to summarize model generalization performance evaluation and topic evolution evaluation methods, as well as providing comparative experimental results for different models. To conclude the review, we pose some open questions and discuss possible future research directions.