Micro-blog Post Topic Drift Detection Based on LDA Model

Quanchao Liu, Heyan Huang, Chong Feng

Abstract: Micro-blog posts imply a large number of topics, which contain a lot of useful information as well as a lot of junk information making the micro-blog post topic a characteristic of high drift. The changes of micro-blog post topic over time and noises introduced with the increase of the number of micro-blog posts are two main aspects of micro-blog post topic drift. We propose a method of topic drift detection based on LDA model, using Gibbs sampling algorithm to obtain the probability distribution of micro-blog post words based on words correlation, identifying the topic boundary in dynamic constant method, extracting topic words by computing lexical information entropy in the topic field, and detecting the topic drift by topic words sequence alignment based on discrete-time model. According to the experiment on topic drift detection based on LDA model, we find our method very effective in micro-blog post topic drift detection.