http://www.bibsonomy.org/burst/user/renew/movie+opinionBibSonomy BuRST Feed for /user/renew/movie+opinion2008-07-26T21:29:26+02:00SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/2aa760f47d7e925c6e66d6fa720b3dfe7/renewrenew2008-02-22T21:11:03+01:00opinion mining movie review <span style="color:#555555;">Noriaki <a href="http://www.bibsonomy.org/author/Sato">Sato</a> and Michiko <a href="http://www.bibsonomy.org/author/Anse">Anse</a> and Tsutomu <a href="http://www.bibsonomy.org/author/Tabe">Tabe</a> </span><em>Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design</em>(<em>2007</em>)Fri Feb 22 21:11:03 CET 2008Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design526--534A Method for Constructing a Movie-Selection Support System Based on Kansei Engineering2007opinion mining movie review 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.Movie review mining and summarizationhttp://www.bibsonomy.org/bibtex/2fda331d7f37683a6b4a06fd1b6f7c3da/renewrenew2008-02-22T20:58:45+01:00opinion mining summarization movie review <span style="color:#555555;">Li <a href="http://www.bibsonomy.org/author/Zhuang">Zhuang</a> and Feng <a href="http://www.bibsonomy.org/author/Jing">Jing</a> and Xiao-Yan <a href="http://www.bibsonomy.org/author/Zhu">Zhu</a> </span><em>CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management, </em><em>page43--50. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2006</em>)Fri Feb 22 20:58:45 CET 2008New York, NY, USACIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management43--50Movie review mining and summarization2006opinion mining summarization movie review 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.