Microblog Hot Topic Evolution Based on Improved On-Line Biterm Topic Model.

Topic evolution analysis is one of the research hotspots of public opinion monitoring. The evolution analysis of microblog hot topics is of great practical significance to network users and network regulators. To solve the problem of OBTM topic mixing and high probability of redundant words, the OBTM based on topic labels and prior parameters (LPOBTM) is proposed in this paper. According to the topic labels, the microblog text set is divided into two types of data sets with and without topic labels. Different document-topic prior parameters are set. Based on document-topic probability distribution in the previous time slice, the intensity ranking of all topics is carried out by drawing lessons from the Sigmod function. Thus, the prior parameter calculation method of topic-word distribution on current time slice is optimized. The experimental results show that LPOBTM can describe the content evolution of topics more accurately, and has lower model perplexity. [ABSTRACT FROM AUTHOR]