| Title | ASEM: Mining aspects and sentiment of events from microblog |
| Authors | Wang, Ruhui Huang, Weijing Chen, Wei Wang, Tengjiao Lei, Kai |
| Affiliation | SPCCTA, School of Electronics and Computer Engineering, Peking University, China Key Laboratory of High Confidence Software Technologies (Ministry of Education), EECS PKU, China |
| Issue Date | 2015 |
| Publisher | 24th ACM International Conference on Information and Knowledge Management, CIKM 2015 |
| Citation | 24th ACM International Conference on Information and Knowledge Management, CIKM 2015.Melbourne, VIC, Australia,2015/10/17,19-23-Oct-2015(1923-1926). |
| Abstract | Microblogs contain the most up-to-date and abundant opinion information on current events. Aspect-based opinion mining is a good way to get a comprehensive summarization of events. The most popular aspect based opinion mining models are used in the field of product and service. However, existing models are not suitable for event mining. In this paper we propose a novel probabilistic generative model (ASEM) to simultaneously discover aspects and the specified opinions. ASEM incorporate a sequence labeling model(CRF) into a generative topic model. Additionally, we adopt a set of features for separating aspects and sentiments. Moreover, we novelly present a continuously learning model. It can utilize the knowledge of one event to learn another, and get a better performance. We use five real world events to do experiment. The experimental results show that ASEM extracts aspects and sentiments well, and ASEM outperforms other state-of-art models and the intuitive two-step method. ? 2015 ACM. |
| URI | http://hdl.handle.net/20.500.11897/436556 |
| ISSN | 9781450337946 |
| DOI | 10.1145/2806416.2806622 |
| Indexed | EI |
| Appears in Collections: | 待认领 |
