Affective computing
How Convincing is Mr. Data's Smile: Affective Expressions of Machines
User Modeling and User-Adapted Interaction
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
Automatic Generation of Computer Animation Conveying Impressions of News Articles
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I
Fair news reader: recommending news articles with different sentiments based on user preference
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Design and evaluation of a music retrieval scheme that adapts to the user's impressions
UM'05 Proceedings of the 10th international conference on User Modeling
Proposal of impression mining from news articles
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
User preference modeling based on interest and impressions for news portal site systems
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Improving a method for quantifying readers' impressions of news articles with a regression equation
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
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This paper focuses on the impressions that people get from reading articles in newspapers. We have already proposed web application systems that extract and use several types of impressions from news articles. However, the types of impressions extracted and used in these systems were intuitively defined by us on the basis of a basic emotion model, which the well-known psychologist Robert Plutchik proposed to represent human emotions. That is, the characteristics of news articles that result in different impressions have not been taken into consideration in much detail. Therefore, we have tried to design one or more impression scales suitable for assessing impressions generated by news articles. First, we conducted nine experiments in each of which 100 people read ten news articles and indicated their impressions on 42 five-point scales, where 42 impression-related words such as "happy" and "strained" were assigned for the 42 scales. Consequently, we obtained impression-estimation data for the 42 impression-related words. Next, we applied factor and cluster analysis to these impression-estimation data, and analyzed similarities among the impression-related words in terms of their scores. In our results, the words that convey similar impressions are classified into a single group and the words that convey opposite impressions are classified into different groups of words. Finally, we designed six impression scales suitable for assessing impressions generated by news articles on the basis of these results, each of which consisted of two contrasting groups of impression-related words.