Date of Original Version
Copyright © SIAM
Abstract or Description
In this paper, we propose the CBS topic model, a probabilistic graphical model, to derive the user communities in microblogging networks based on the sentiments they express on their generated content and behaviors they adopt. As a topic model, CBS can uncover hidden topics and derive user topic distribution. In addition, our model associates topicspecific sentiments and behaviors with each user community. Notably, CBS has a general framework that accommodates multiple types of behaviors simultaneously. Our experiments on two Twitter datasets show that the CBS model can effectively mine the representative behaviors and emotional topics for each community. We also demonstrate that CBS model perform as well as other state-of-the-art models in modeling topics, but outperforms the rest in mining user communities.
Proceedings of the 2014 SIAM International Conference on Data Mining, 479-487.