Information Retrieval
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Automatic expansion of feature-level opinion lexicons
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
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Feature-based opinion extraction is a task related to information extraction, which consists of extracting structured opinions on features of some object from reviews or other subjective textual sources. Over the last years, this problem has been studied by some researchers, generally in an unsupervised, domain-independent manner. As opposed to that, in this work we propose a redefinition of the problem from a more practical point of view, and describe a domain-specific, resource-based opinion extraction system. We focus on the description and generation of those resources, and briefly report the extraction system architecture and a few initial experiments. The results suggest that domain-specific knowledge is a valuable resource in order to build precise opinion extraction systems.