Abstract: Event can spread rapidly in the form of topic microblog and make enormous influence.Therefore,the analysis for the users and discovering groups with different interesting and sentiments in the topic discussion obtain the concern of the government and enterprises.The generated content and relationship between the users are often separated in the current methods on community detection,which have no semantic information.Though some methods have combined the two factors,they fail to take account of the behavior information and sentiment information which exist in microblog,and they are not well to mine the groups in the microblog topic discussion.We proposed a group partition model called BP-STG which takes the text information,social contacts,text sentiment information and the users’ behavior into consideration.We presented a Gibbs sampling implementation for inference of our model,mining only different interest groups,but also the sentiment distribution and participants’ activeness and behavior information in a group.Besides,our model can be extended to many texts associated with a group of people such as E-mails and forum posts.Experimental results on actual dataset show that BP-STG model can offer an effective solution to group partition in topic-related microblogging spreading and provide more meaningful semantic information than the state-of-the-art model.

Key words: Microblogging topic,Probability generation model,Groups partition,Sentiment information,Behavior pattern