Improved relevance ranking in WebGather
Journal of Computer Science and Technology
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On Learning Parsimonious Models for Extracting Consumer Opinions
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 3 - Volume 03
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Journal of Artificial Intelligence Research
Ranking and scoring using empirical risk minimization
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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This paper is concerned with the problem of mining commodity information from threaded Chinese customer reviews. Chinese online commodity forums, which are developing rapidly, provide a good environment for customers to share reviews. However, due to noises and navigational limitations, it is hard to have a clear view of a commodity from thousands of related reviews. Further more, due to different characters between Chinese and English, Researching approaches may vary a lot. This paper aims to automatically mine out key information from commodity reviews. An effective algorithm, i.e. Chinese Commodity Review Miner (CCRM) is proposed. The algorithm can be divided into two parts. First, we propose an efficient rule based algorithm for commodity feature extraction as well as a probabilistic model for feature ranking. Second, we propose a top-to-down algorithm to reorganize the extracted features into hierarchical structure. A prototype system based on CCRM is also implemented. Using CCRM, users can easily acquire the outline of a commodity, and navigate freely in it.