Modeling Discrete Dynamic Topics

Topic modeling is an important area which aims at indexing and exploring massive data streams. In this paper we introduce a discrete Dynamic Topic Modeling (dDTM) algorithm, which is able to model a dynamic topic that is not necessarily present over all time slices in a stream of documents. Our proposed model has applications in modeling dynamic topics of rapidly changing and less structured data, such as online microblogs and news streams. Our results show that the topical chains (i.e., evolution of topics) computed by our algorithm is more representative of the contents of documents than the original Dynamic Topic Modeling (DTM) in terms of likelihood on held-out data. Furthermore, we show that our method is effective in identifying emerging trends in streaming data