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Neural networks for pattern recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
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EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Utility scoring of product reviews
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Do online reviews affect product sales? The role of reviewer characteristics and temporal effects
Information Technology and Management
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Electronic Commerce Research and Applications
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Decision Support Systems
Electronic Commerce Research and Applications
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Electronic Commerce Research and Applications
International Journal of Electronic Commerce
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ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Automatically assessing the post quality in online discussions on software
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Consideration sets in online shopping environments: the effects of search tool and information load
Electronic Commerce Research and Applications
Recommender systems from "words of few mouths"
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Electronic Word-of-Mouth (eWOM) is growing exponentially with the rapid development of electronic commerce. As a result, consumers are increasingly crowded by a huge amount of eWOM contents and therefore there is a need to automatically recommend eWOM contents that are helpful to them. Existing helpfulness assessment approaches that deterministically estimate the helpfulness of eWOM contents lack a generative formulation and are limited to the training set that has been voted by many readers. This article presents a rigorous probabilistic framework for inferring the “helpfulness” of eWOM contents which can build a “helpfulness” model from a low number of votes on eWOM contents. Furthermore, we introduce a measurement, “helpfulness” bias, as the benchmark for the “helpfulness” of eWOM documents. We also propose a model that exploits the graphical model and expectation maximization algorithm, under this probabilistic framework, to demonstrate the versatility of our framework. Our algorithm is compared experimentally to other existing helpfulness discovering algorithms and the experimental results show that our framework can effectively model the helpfulness of eWOM contents better than other approaches, and therefore indicate the capability of our framework to recommend helpful eWOMs to potential consumers.