Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Robustness beyond shallowness: incremental deep parsing
Natural Language Engineering
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
You Are Who You Talk To: Detecting Roles in Usenet Newsgroups
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
Identifying and analyzing judgment opinions
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Informed Recommender: Basing Recommendations on Consumer Product Reviews
IEEE Intelligent Systems
The utility of linguistic rules in opinion mining
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
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In this paper, we propose the use of fine-grained information such as opinions and suggestions extracted from users' reviews about products, in order to improve a recommendation system. While typical recommender systems compare a user profile with some reference characteristics to rate unseen items, they rarely make use of the content of reviews users have done on a given product. In this paper, we show how we applied an opinion extraction system to extract opinions but also suggestions from the content of the reviews, use the results to compare other products with the reviewed one, and eventually recommend a better product to the user.