Key Person Analysis in Persistent topic with online Forum

Abstract

The influence of users on online Forum should not be simply determined by the global network topology but rather in the corresponding local network with the user’s active range and semantic relation. Current analysis methods mostly focus on urgent topics while ignoring persistent topics, but persistent topics often have important implications for public opinion analysis. Therefore, this paper explores key person analysis in persistent topics on online Forum based on semantics. First, the interaction data are partitioned into subsets according to month, and the Latent Dirichlet Allocation (LDA) and filtering strategy are used to identify the topics from each partition. Then, we try to associate one topic with the adjacent time slice, which fulfills the criterion of having high similarity degree. On the basis of such topics, persistent topics are defined that exist for a sufficient number of periods. Following this, the paper provides the commitment function update criteria for the persistent topic social network (PTSN) based on the semantic and the sentiment weighted node position (SWNP) to identify the key person who has the most influence in the field. Finally, the emotional tendency analysis is applied to correct the results. The methods in real data sets validate the effectiveness of these methods.