Extracting opinion topics for Chinese opinions using dependence grammar

Authors:
Guang Qiu;Kangmiao Liu;Jiajun Bu;Chun Chen;Zhiming Kang
Affiliations:
Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China
Venue:
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
Year:
2007

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Abstract

Previous work on opinion/sentiment mining focuses only on sentiment classification with the postulation that topics are identified a prior. However, this assumption often fails in reality. In advertising, topics on which users are commenting are crucial as corresponding advertisements can only be promoted when advertisers have the idea of what users are referring to. In this paper, we propose a rule-based approach to extracting topics from opinion sentences given these sentences identified from texts in advance. We build up a sentiment dictionary and define several rules based on the syntactic roles of words using the Dependence Grammar which is considered to be more suitable for Chinese natural language parsing. The experiments show encouraging results.