CLUSM: An Unsupervised Model for Microblog Sentiment Analysis Incorporating Link Information

Microblog has become a popular platform for people to share their ideas, information and opinions. In addition to textual content data, social relations and user behaviors in microblog provide us additional link information, which can be used to improve the performance of sentiment analysis. However, traditional sentiment analysis approaches either focus on the plain text, or make simple use of links without distinguishing different effects of different types of links. As a result, the performance of sentiment analysis on microblog can not achieve obvious improvement. In this paper, we are the first to divide the links between microblogs into three classes. We further propose an unsupervised model called Content and Link Unsupervised Sentiment Model (CLUSM). CLUSM focuses on microblog sentiment analysis by incorporating the above three types of links. Comprehensive experiments were conducted to investigate the performance of our method. Experimental results showed that our proposed model outperformed the state of the art.