Modeling and Analyzing Topic Evolution
一种话题演化建模与分析方法

Topic evolution of network public opinions is investigated. By treating topics as a set of correlated sub-topics, a topic evolution model is proposed, consisting of sub-topic detection and correlation analysis. Furthermore, a sub-topic detection algorithm based on OLDA is presented with Bayesian model selection for the appropriate topic numbers and parameters estimation via Gibbs sampling. The correlations are further defined for analysis of topic evolution, including emergence, extinction, development, merge and split of sub-topics. The method is experimentally verified to be efficient for detecting topic evolution of network public opinions.