Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Stochastic models for the Web graph
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Random Evolution in Massive Graphs
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Self-selection, slipping, salvaging, slacking, and stoning: the impacts of negative feedback at eBay
Proceedings of the 6th ACM conference on Electronic commerce
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Utility scoring of product reviews
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
ETF: extended tensor factorization model for personalizing prediction of review helpfulness
Proceedings of the fifth ACM international conference on Web search and data mining
Identifying helpful reviews based on customer's mentions about experiences
Expert Systems with Applications: An International Journal
Information Retrieval in the Commentsphere
ACM Transactions on Intelligent Systems and Technology (TIST)
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With the number of online reviews growing rapidly, it is increasingly difficult to digest all the information within limited time. To help users efficiently get concise information about a product, researchers have studied algorithms for automated opinion summarization. However, users might expect to further read detailed high-quality reviews in addition to a review outline. This raises another interesting problem not well studied yet: how to discover high quality product reviews? Previous research examined various properties of a product review to predict its quality. In this paper, we further explore this topic by incorporating another information resource: the behavior of review authors in an e-commerce community. First, we perform a high-level analysis on two kinds of data: product reviews and deal transactions. According to the results of this analysis, three features, including personal reputation, seller degree and expertise degree, are studied to assess the quality of a review from a credibility and expertise perspective. Our analysis shows that these features are strongly related to review quality and that they can help uncover review spamming by sellers. Furthermore, we propose a simulation model based on the above findings. The model is able to generate the basic properties of the review community, especially when the above three features are taken into account.