Quality information and knowledge
Quality information and knowledge
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
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
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
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
Introduction to Information Retrieval
Introduction to Information Retrieval
Support Vector Machines
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
Review recommendation: personalized prediction of the quality of online reviews
Proceedings of the 20th ACM international conference on Information and knowledge management
ETF: extended tensor factorization model for personalizing prediction of review helpfulness
Proceedings of the fifth ACM international conference on Web search and data mining
Review rating prediction based on the content and weighting strong social relation of reviewers
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
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The prevalence of Web2.0 makes the Web an invaluable source of information. For instance, product reviews composed collaboratively by many independent Internet reviewers can help consumers make purchase decisions and enable manufactures to improve their business strategies. As the number of reviews is increasing exponentially, opinion mining is needed to identify important reviews and opinions for users. Most opinion mining approaches try to extract sentimental or bipolar expressions from a large volume of reviews. However, the mining process often ignores the quality of each review and may retrieve useless or even noisy reviews. In this paper, we propose a method for evaluating the quality of information in product reviews. We treat review quality evaluation 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.