Information retrieval as statistical translation
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Hot Item Mining and Summarization from Multiple Auction Web Sites
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning to extract and summarize hot item features from multiple auction web sites
Knowledge and Information Systems
Classifying web review opinions for consumer product analysis
Proceedings of the 11th International Conference on Electronic Commerce
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Sentiment retrieval using generative models
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Expanding domain sentiment lexicon through double propagation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Extracting and ranking product features in opinion documents
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Integrating web feed opinions into a corporate data warehouse
Proceedings of the 2nd International Workshop on Business intelligencE and the WEB
Storing and analysing voice of the market data in the corporate data warehouse
Information Systems Frontiers
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In this paper, we propose a methodology for obtaining a probabilistic ranking of product features from a customer review collection. Our approach mainly relies on an entailment model between opinion and feature words, and suggest that in a probabilistic opinion model of words learned from an opinion corpus, feature words must be the most probable words generated from that model (even more than opinion words themselves). In this paper, we also devise a new model for ranking corpus-based opinion words. We have evaluated our approach on a set of customer reviews of five products obtaining encouraging results.