Major topic detection and its application to opinion summarization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
The effect of negation on sentiment analysis and retrieval effectiveness
Proceedings of the 18th ACM conference on Information and knowledge management
Locally contextualized smoothing of language models for sentiment sentence retrieval
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Domain Specific Opinion Retrieval
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
ACM SIGIR Forum
Information Retrieval on the Blogosphere
Foundations and Trends in Information Retrieval
Sentiment classification based on phonetic characteristics
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Sentiment diversification with different biases
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Opinion retrieval is a document retrieving and ranking process. A relevant document must be relevant to the query and contain opinions toward the query. Opinion polarity classification is an extension of opinion retrieval. It classifies the retrieved document as positive, negative or mixed, according to the overall polarity of the query relevant opinions in the document. This paper (1) proposes several new techniques that help improve the effectiveness of an existing opinion retrieval system; (2) presents a novel two-stage model to solve the opinion polarity classification problem. In this model, every query relevant opinionated sentence in a document retrieved by our opinion retrieval system is classified as positive or negative respectively by a SVM classifier. Then a second classifier determines the overall opinion polarity of the document. Experimental results show that both the opinion retrieval system with the proposed opinion retrieval techniques and the polarity classification model outperformed the best reported systems respectively.