Leveraging the web context for context-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 traini...

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Bibliographic Details
Published in:2009 2nd IEEE International Conference on Computer Science and Information Technology pp. 467 - 471
Main Authors: Lau, R.Y.K., Lai, C.L., Yuefeng Li
Format: Conference Proceeding
Language:English
Published: IEEE 01.01.2009
Subjects:
Blogs
Business Intelligence
Context modeling
Context-Sensitive Text Mining
Data mining
Electronic mail
Inferential Language Modeling
Information systems
Information technology
Motion pictures
Opinion Mining
Sentiment Analysis
Statistical learning
Text mining
Web pages
ISBN:1424445191, 9781424445196
Online Access:Get full text
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Summary: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.
ISBN:1424445191
9781424445196
DOI:10.1109/ICCSIT.2009.5234821