Modern Information Retrieval
The Journal of Machine Learning Research
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
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Q2C@UST: our winning solution to query classification in KDDCUP 2005
ACM SIGKDD Explorations Newsletter
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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
Ranking opinionated blog posts using OpinionFinder
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
Improve the effectiveness of the opinion retrieval and opinion polarity classification
Proceedings of the 17th ACM conference on Information and knowledge management
Sentiment retrieval using generative models
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
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Opinion retrieval is a novel information retrieval task and has attracted a great deal of attention with the rapid increase of online opinionated information. Most previous work adopts the classical two stage framework, i.e., first retrieving topic relevant documents and then re-ranking them according to opinion relevance. However, none has considered the problem of domain coherence between queries and topic relevant documents. In this work, we propose to address this problem based on the similarity measure of the usage of opinion words (which users employ to express opinions). Our work is based on the observation that the opinion words are domain dependent. We reformulate this problem as measuring the opinion similarity between domain opinion models of queries and document opinion models. Opinion model is constructed to capture the distribution of opinion words. The basic idea is that if a document has high opinion similarity with a domain opinion model, it indicates that it is not only opinionated but also in the same domain with the query (i.e., domain coherence). Experimental results show that our approach performs comparatively with the state-of-the-art work.