Abstract: A computational model for online topic evolution mining was established through a latent semantic analysis process on textual data. Topical evolutionary analysis was achieved by tracking the topic trends in different time-slices.In this paper, Latent Dirichlet Allocation (LDA) was extended to the context of online text streams, and an online LDA model was proposed and implemented as well. The main idea is to use the posterior of topirword distribution of each time-slice to influence the inference of the next time-slice, which also maintains the relevance between the topics. The topirword and document-topic distributions arc inferenced by incremental Gibbs algorithm. Kullback Leibler (KI)relative entropy is uesd to measure the similarity between topics in order to identify topic genetic and topic mutation. Experiments show that the proposed model can discover meaningful topical evolution trends both on English and Chinese corpus.

Key words: Topic model, LDA, Evolution, Public opinion