%0 Journal Article %1 keyhere %A Sato, Noriaki %A Anse, Michiko %A Tabe, Tsutomu %D 2007 %J Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design %K mining movie opinion review %P 526--534 %T A Method for Constructing a Movie-Selection Support System Based on Kansei Engineering %U http://dx.doi.org/10.1007/978-3-540-73345-4_60 %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.

@article{keyhere, abstract = {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.}, added-at = {2008-02-22T21:11:03.000+0100}, author = {Sato, Noriaki and Anse, Michiko and Tabe, Tsutomu}, biburl = {https://www.bibsonomy.org/bibtex/2aa760f47d7e925c6e66d6fa720b3dfe7/renew}, description = {SpringerLink - Book Chapter}, interhash = {bdb330c58550d1c0cc222ad59bb337de}, intrahash = {aa760f47d7e925c6e66d6fa720b3dfe7}, journal = {Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design}, keywords = {mining movie opinion review}, pages = {526--534}, timestamp = {2008-02-26T13:44:33.000+0100}, title = {A Method for Constructing a Movie-Selection Support System Based on Kansei Engineering}, url = {http://dx.doi.org/10.1007/978-3-540-73345-4_60}, year = 2007 }