A Topic Discovery Model Based on Content and Emotion in Microblogs

With the boom of social media, the real-world topics are often discussed on the Web via microblogs, which forms oceans of short texts hidden various topics. These topics in microblogs often contain different content and different emotion. How to automatically discover these topics from the high volume of microblogs is a significant question. Although traditional topic discovery methods can discover topics from texts, most of them pay less attentions on short texts, the emotion of topics and some specific structural features, such as comments and forwarding in microblogs. To solve the above issues, we face the following challenging issues: 1) how to extend the sparse semantics for short texts, 2) how to use and obtain the emotion contained by these topics. Herein, we propose a topic discovery model, which combines textual content and emotion in microblogs and their feedback (comments and forwarding). In the firstly, we extend microblogs´ sparse semantics with their feedback, and divide the dataset into two structures, content and emotional labels. Secondly, we incorporate these two different structures into a uniformed probabilistic model and do our topic discovery. At last, we extract the content and emotion expression of each topic. Experimental results demonstrate our method can efficiently discovery topics which both contain varied content and emotion with higher recall rate and precision.