IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
A phrase-based, joint probability model for statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
How opinions are received by online communities: a case study on amazon.com helpfulness votes
Proceedings of the 18th international conference on World wide web
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A Grammar-Based Unsupervised Method of Mining Volitive Words
IALP '10 Proceedings of the 2010 International Conference on Asian Language Processing
Aspect and sentiment unification model for online review analysis
Proceedings of the fourth ACM international conference on Web search and data mining
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This paper focuses on exploring the features of product reviews that satisfy users, by which to improve the automatic helpfulness voting for the reviews on commercial websites. Compared to the previous work, which single-mindedly adopts the textual features to assess the review helpfulness, we propose that user preferences are more explicit clues to infer the opinions of users on the review helpfulness. By using the user-preference based features, we firstly implement a binary helpfulness based review classification system to divide helpful reviews and useless, and on the basis, we secondly build a Ranking SVM based automatic helpfulness voting system (AHV) which rank reviews based on their helpfulness. Experiments used a large scale dataset containing over 34,266 reviews on 1289 products to test the systems, which achieves promising performances with accuracy of up to 0.72 and NDCG@10 of 0.25, and at least 9% accuracy improvement compared to the textual-feature based helpfulness assessment.