Leveraging the Web Contezt for Contezt-Sensitive Opinion Mining
Existing automated opinion mining methods either employ a static lexicon-based approach or a supervised learning approach. Nevertheless, the former method often fails to identify context-sensitive semantics of the opinion words, and the latter approach requires a large number of human labeled training examples. The main contribution of this paper is the illustration of a novel opinion mining method underpinned by context-sensitive text mining and inferential language modeling to improve the effectiveness of opinion mining. Our initial experiments show that the proposed the inferential opinion mining method outperforms the purely lexicon-based opinion finding method in terms of several benchmark measures. Our research opens the door to the development of more effective opinion mining method to discover business intelligence from the Web knowledge base.
Opinion Mining Sentiment Analysis Contezt- Sensitive Tezt Mining Inferential Language Modeling Business Intelligence
Raymond Y.K. Lau C.L. Lai Yuefeng Li
Department of Information Systems City University of Hong Kong Tat Chee Avenue, Kowloon Hong Kong School of Information Technology Queensland University of Technology GPO Box 2434, Brisbane, Qld 400
国际会议
北京
英文
1128-1132
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）