Exploiting Community Emotion for Microblog Event Detection

Gaoyan Ou, Wei Chen, Tengjiao Wang, Zhongyu Wei, Binyang Li, Dongqing Yang, Kam-Fai Wong

Abstract: Microblog has become a major platform for information about real-world events. Automatically discovering realworld events from microblog has attracted the attention of many researchers. However, most of existing work ignore the importance of emotion information for event detection. We argue that people’s emotional reactions immediately reflect the occurringofreal-worldeventsand shouldbeimportant for event detection. In this study, we focus on the problem of communityrelated event detection by community emotions. To address the problem, we propose a novel framework which include the following three key components: microblog emotion classification, community emotion aggregation and community emotion burst detection. We evaluate our approach on real microblog data sets. Experimental results demonstrate the effectiveness of the proposed framework.