People Opinion Topic Model: Opinion based User Clustering in Social Networks

Hongxu Chen, Hongzhi Yin, Xue Li, Meng Wang, Weitong Chen, Tong Chen

Abstract: Mining various hot discussed topics and corresponding opinions from different groups of people in social media (e.g., Twitter) is very useful. For example, a decision maker in a company wants to know how different groups of people (customers, staff, competitors, etc.) think about their services, facilities, and things happened around. In this paper, we are focusing on the problem of finding opinion variations based on different groups of people and introducing the concept of opinion based community detection. Further, we also introduce a generative graphic model, namely People Opinion Topic (POT) model, which detects social communities, associated hot discussed topics, and perform sentiment analysis simultaneously by modelling user's social connections, common interests, and opinions in a unified way. This paper is the first attempt to study community and opinion mining together. Compared with traditional social communities detection, the detected communities by POT model are more interpretable and meaningful. In addition, we further analyse how diverse opinions distributed and propagated among various social communities. Experiments on real twitter dataset indicate our model is effective.