Combining parametric and nonparametric topic model to discover microblog event

Compared with traditional news media, microblog holds overwhelming superiority in fast-diffusion and comprehensive coverage of topics. Microblog becomes an effective, particular and important carrier of affair information and many other text analysis tasks, e.g., event discovering based on microblog have special significance. Common tools of content analysis, such as topic model, however, experience severe data sparsity problems due to short length of microblog. Following previous researchers´ idea, such as separating personal interest post from global event post, we further differentiate general topics from event topics and adopt nonparametric method to model the birth and death of event. We conduct experiments on Twitter data set, and the experimental results demonstrate that our method can not only discover event effectively, but also mine higher quality general topics.