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%0 Book Section
%A Dziczkowski, Grzegorz & Wegrzyn-Wolska, Katarzyna
%D 2007
%T RRSS - Rating Reviews Support System Purpose Built for Movies Recommendation
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%B Advances in Intelligent Web Mastering
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%P 87--93
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%3 incollection
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%F keyhere
%K movie recommendation system 
%X This paper describes the part of a recommendation system designed for the recognition of film reviews (RRSS). Such a system allows the automatic collection, evaluation and rating of reviews and opinions of the movies. First the system searches andretrieves texts supposed to be movie reviews from the Internet. Subsequently the system carries out an evaluation and ratingof the movie reviews. Finally, the system automatically associates a digital assessment with each review. The goal of thesystem is to give the score of reviews associated with the user who wrote them. All of this data is the input to the cognitiveengine. Data from our base allows the making of correspondences, which are required for cognitive algorithms to improve, advancedrecommending functionalities for e-business and e-purchase websites. In this paper we will describe the different methodson automatically identifying opinions using natural language knowledge and techniques of classification.
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%U http://dx.doi.org/10.1007/978-3-540-72575-6_14
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%0 Journal Article
%A Sato, Noriaki; Anse, Michiko & Tabe, Tsutomu
%D 2007
%T A Method for Constructing a Movie-Selection Support System Based on Kansei Engineering
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%B Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design
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%6
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%P 526--534
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%K mining movie opinion review 
%X When a person requests, for example, “I want to see a bright and exciting movie,” the words “bright” and “exciting” are called Kansei keywords. With a retrieval system to retrieve recommended movies using these Kansei keywords, a viewer will be able to select movies that fit the Kansei without actually having to view samples or previews of the movies. The purpose of this research is to clarify a method toconstruct a support system capable of selecting movies that fit the viewer’s Kansei, and to verify the effectiveness of this method based on Kansei engineering, for the selection of recommended movies. To accomplish this, we extract the features of a movie using factorfactoranalysis from data from a Semantic Differential Gauge questionnaire, then link the viewer’s Kansei with the features using multiple linear regression analysis. After constructing a prototype � system to verify the effectiveness,ten examinees viewed a movie selected by the prototype � system. “The selected movie fit the Kansei” at a level of about 70percent.
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%U http://dx.doi.org/10.1007/978-3-540-73345-4_60
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%0 Conference Proceedings
%A Zhuang, Li; Jing, Feng & Zhu, Xiao-Yan
%D 2006
%T Movie review mining and summarization
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%B CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management
%C New York, NY, USA
%I ACM
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%P 43--50
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%@ 1-59593-433-2
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%F 1183625
%K mining movie opinion review summarization 
%X With the flourish of the Web, online review is becoming a more and more useful and important information resource for people. As a result, automatic review mining and summarization has become a hot research topic recently. Different from traditional text summarization, review mining and summarization aims at extracting the features on which the reviewers express their opinions and determining whether the opinions are positive or negative. In this paper, we focus on a specific domain - movie review. A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis and movie knowledge. The experimental results show the effectiveness of the proposed approach in movie review mining and summarization.
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%U http://portal.acm.org/citation.cfm?id=1183614.1183625
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