Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Japanese morphological analyzer using word co-occurrence: JTAG
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
Identifying comparative sentences in text documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Mining comparative sentences and relations
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Finding Comparative Facts and Aspects for Judging the Credibility of Uncertain Facts
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Discovering intermediate entities from two examples by using web search engine indices
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Privacy preservation of user history graph
WISTP'12 Proceedings of the 6th IFIP WG 11.2 international conference on Information Security Theory and Practice: security, privacy and trust in computing systems and ambient intelligent ecosystems
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This paper proposes a method for ranking entities (e.g. products, people, etc.) that uses the comparative sentences described in text such as reviews, blogs, etc. as an indicator of an individual entity's value. A comparative sentence expresses a relation between two entities. The comparative sentence "The quality of A is better than that of B" is expressed by the comparative relation {A,B,quality,better}. Given a query (set of queries), the proposed method automatically finds the competitive entities and extracts the comparative relations among them. From the vast amount of comparative relations so extracted, the proposed method then generates a graph modeling the behavior of a "potential customer" to assess the relative importance of every entity against its competitors. These ranking results help potential customers to know the position of the entity among related entities and decide which one to choose.