Class-based n-gram models of natural language
Computational Linguistics
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 11th international conference on World Wide Web
Vector space model of information retrieval: a reevaluation
SIGIR '84 Proceedings of the 7th annual international ACM SIGIR conference on Research and development in information retrieval
Genre Classification and Domain Transfer for Information Filtering
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
A Unified Framework for Web Link Analysis
WISE '02 Proceedings of the 3rd International Conference on Web Information Systems Engineering
Implicit link analysis for small web search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Recognizing subjectivity: a case study in manual tagging
Natural Language Engineering
Predicting the semantic orientation of adjectives
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
Noun-phrase analysis in unrestricted text for information retrieval
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Link fusion: a unified link analysis framework for multi-type interrelated data objects
Proceedings of the 13th 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
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Applying Visualisation Techniques in Novel Domains
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
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
Learning to disambiguate potentially subjective expressions
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Subjectivity Categorization of Weblog with Part-of-Speech Based Smoothing
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Sentiment retrieval using generative models
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
ETF: extended tensor factorization model for personalizing prediction of review helpfulness
Proceedings of the fifth ACM international conference on Web search and data mining
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In the Web 2.0 era, internet users contribute a large amount of online content. Product review is a good example. Since these phenomena are distributed all over shopping sites, weblogs, forums etc., most people have to rely on general search engines to discover and digest others' comments. While conventional search engines work well in many situations, it's not sufficient for users to gather such information. The reasons include but are not limited to: 1) the ranking strategy does not incorporate product reviews' inherent characteristics, e.g., sentiment orientation; 2) the snippets are neither indicative nor descriptive of user opinions. In this paper, we propose a feasible solution to enhance the experience of product review search. Based on this approach, a system named "Improved Product Review Search (IPRS)" is implemented on the ground of a general search engine. Given a query on a product, our system is capable of: 1) automatically identifying user opinion segments in a whole article; 2) ranking opinions by incorporating both the sentiment orientation and the topics expressed in reviews; 3) generating readable review snippets to indicate user sentiment orientations; 4) easily comparing products based on a visualization of opinions. Both results of a usability study and an automatic evaluation show that our system is able to assist users quickly understand the product reviews within limited time.