Word association norms, mutual information, and lexicography
Computational Linguistics
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
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
Opinion mining of customer feedback data on the web
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Music review classification enhanced by semantic information
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Effectively and efficiently supporting crowd-enabled databases via NoSQL paradigms
Proceedings of the 3rd International Workshop on Semantic Search Over the Web
Semisupervised learning based opinion summarization and classification for online product reviews
Applied Computational Intelligence and Soft Computing
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When most people buy the products, they inquire about other people's opinion and refer to recommended product. Today, the result of explosive development of the Web makes it easy to consult other people's opinion information. These variety of opinion data are not only useful to customers, but also manufacturers. As a result, opinion mining research to analyze opinion data on the web has become a popular topic recently. In this paper, we proposed opinion mining method for product reviews. In our approach, we first do POS tagging on each review sentence, and we extract feature and opinion words in form of transaction data. Then we discover association rules of needed type from the transaction data, and provide information that is summarized advantages and disadvantages using PMI-IR algorithm.