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
A probabilistic model of information retrieval: development and comparative experiments
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
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
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
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
An effective statistical approach to blog post opinion retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Systematic analysis of centralized online reputation systems
Decision Support Systems
Decision Support Systems
Identifying helpful online reviews: A product designer's perspective
Computer-Aided Design
A multidimensional analysis of data quality for credit risk management: New insights and challenges
Information and Management
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The ubiquity of Web2.0 makes the Web an invaluable source of business information. For instance, product reviews composed collaboratively by many independent Internet reviewers can help consumers make purchase decisions and enable enterprises to improve their business strategies. As the number of reviews is increasing exponentially, opinion mining and retrieval techniques are needed to identify important reviews and opinions to answer users' queries. Most opinion mining and retrieval approaches try to extract sentimental or bipolar expressions from a large volume of reviews. However, the process often ignores the quality of each review and may retrieve useless or even noisy documents. In this paper, we propose a method for evaluating the quality of information in product reviews. We treat the evaluation of review quality as a classification problem and employ an effective information quality framework to extract representative review features. Experiments based on an expert-composed data corpus demonstrate that the proposed method outperforms state-of-the-art approaches significantly.