Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Hidden sentiment association in chinese web opinion mining
Proceedings of the 17th international conference on World Wide Web
Expanding domain sentiment lexicon through double propagation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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As the number of customer reviews grows very rapidly, it is essential to summarize useful opinions for buyers, sellers and producers. One key step of opinion mining is feature extraction. Most existing research focus on finding explicit features, only a few attempts have been made to extract implicit features. Nearly all existing research only concentrate on product features, few has paid attention to other features that relates to sellers, services and logistics. Therefore in this paper, we propose a novel co-occurrence association-based method, which aims to extract implicit features in customer reviews and provide more comprehensive and fine-grained mining results.