A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
Incorporating quality metrics in centralized/distributed information retrieval on the World Wide Web
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Judgement of information quality and cognitive authority in the Web
Journal of the American Society for Information Science and Technology
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Designing novel review ranking systems: predicting the usefulness and impact of reviews
Proceedings of the ninth international conference on Electronic commerce
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Analyzing and Detecting Review Spam
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Weighing Stars: Aggregating Online Product Reviews for Intelligent E-commerce Applications
IEEE Intelligent Systems
HelpMeter: A Nonlinear Model for Predicting the Helpfulness of Online Reviews
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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
Web spam identification through language model analysis
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
Is spam an issue for opinionated blog post search?
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Merging multiple criteria to identify suspicious reviews
Proceedings of the fourth ACM conference on Recommender systems
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In the Web 2.0 era, there has been an explosive growth of user-contributed data on the Web. Among the user-contributed data, the sheer volume of online reviews (or comments) provide enterprise with invaluable market intelligence about potential customers' preferences for various products and services. However, there has been growing concerns about the quality of these uncontrolled user-contributed online reviews. Despite numerous research work has been conducted on opinion mining and opinion retrieval, little work has been done to develop effective quality metrics to assess the quality of opinionated expressions. To discover rich and accurate business intelligence from online opinionated expressions, an objective quality-based filtering process is essential for any opinion mining systems. The main contribution of this paper is the design, development, and evaluation of a novel multi-facet quality metric for the assessment of the informativeness of opinionated expressions such as online product reviews. Our preliminary experiments show that the proposed multi-facets quality metric is more effective than a quality assessment approach constructed based on user-generated helpful votes.