Extracting and tracking hot topics of micro-blogs based on improved Latent Dirichlet Allocation

Abstract

Micro-blog has changed people’s life, study, and work styles. Every day, we want to know what public opinion news happens and how it evolves. Extracting and tracking these topics correctly help us better understand the latest public opinions and pay attention to their evolution. To extract topics from Microblog posts accurately, we adopt five unique features of micro-blogs to drive the joint probability distributions of all words and topics, and improve LDA into our topic extraction model(named MF-LDA). To track evolution trend of the topic, we propose a hot topic life cycle model (named HTLCM). We divide the HTLCM into five stages, namely, birth, growth, maturity, decline, and disappearance. The HTLCM determines whether a topic is the candidate hot topic or not and estimates hot topic evolution stages. On the other hand, we propose a hot topic tracking (shorten for HTT) algorithm which integrates MF-LDA and HTLCM. First, the HTT assigns candidate hot topics, which are labeled by HTLCM, to the corresponding time window according to the release time. Second, to obtain the hot topic in each time window, we input Micro-blog posts of each time window into MF-LDA in order. By analyzing changes in these hot topics, we track the changes in their contents. The experiment results show that MF-LDA has a lower perplexity and higher coverage rate than LDA under the same conditions. We conclude parameters of the Transition regions of our proposed HTLCM model. The MR, FR of our proposed HTLCM model are lower than 18%. The average P, R, F of the HTT algorithm are 85.64%, 84.97%, 85.66%, respectively. A practical application on topicFemale driver beats male driver in chengdu shows an excellent effect and practical significance of HTLCM model and HTT algorithm in extracting and tracking hot topics.