A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th 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
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A comparison of sentiment analysis techniques: polarizing movie blogs
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Decision support system for industrial designer based on kansei engineering
IDGD'11 Proceedings of the 4th international conference on Internationalization, design and global development
A retrieval method adaptively reducing user's subjective impression gap
Multimedia Tools and Applications
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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 to construct 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 factoranalysis 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 70 percent.