Dynamic Online HDP model for discovering evolutionary topics from Chinese social texts

User-generated content such as online reviews in social media evolve rapidly over time. To better understand the social media content, users not only want to examine what the topics are, but also want to discover the topic evolution patterns. In this paper, we propose a Dynamic Online Hierarchical Dirichlet Process model (DOHDP) to discover the evolutionary topics for Chinese social texts. In our DOHDP model, the evolutionary processes of topics are considered as evolutions in two levels, i.e. inter-epoch level and intra-epoch level. In inter-epoch level, the corpus of each epoch is modeled with an online HDP topic model, and the social texts are generated in a sequence mode. In the intra-epoch level, the time dependencies of historical epochs are modeled with an exponential decay function in which more recent epochs have a relatively stronger influence on the model parameters than the earlier epoch. Furthermore, we implement our DOHDP model using a two-phase online variational algorithm. Through comparing our DOHDP model with other related topic models on Chinese social media dataset Tianya-80299, the experiment results show that DOHDP model provides the best performance for discovering the evolutionary topics of Chinese social texts.