A flexible content-based image retrieval model and a customizable system for the retrieval of shapes
Journal of the American Society for Information Science and Technology
MEMOSE: search engine for emotions in multimedia documents
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A new model for semantic photograph description combining basic levels and user-assigned descriptors
Journal of Information Science
Analyzing and predicting sentiment of images on the social web
Proceedings of the international conference on Multimedia
Using association rules to discover color-emotion relationships based on social tagging
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Researching emotion: challenges and solutions
Proceedings of the 2011 iConference
Information Processing and Management: an International Journal
On the consistency and features of image similarity
Proceedings of the 4th Information Interaction in Context Symposium
Basic-level categories: A review
Journal of Information Science
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Some documents provoke emotions in people viewing them. Will it be possible to describe emotions consistently and use this information in retrieval systems? We tested collective (statistically aggregated) emotion indexing using images as examples. Considering psychological results, basic emotions are anger, disgust, fear, happiness, and sadness. This study follows an approach developed by Lee and Neal (2007) for music emotion retrieval and applies scroll bars for tagging basic emotions and their intensities. A sample comprising 763 persons tagged emotions caused by images (retrieved from ) applying scroll bars and (linguistic) tags. Using SPSS, we performed descriptive statistics and correlation analysis. For more than half of the images, the test persons have clear emotion favorites. There are prototypical images for given emotions. The document-specific consistency of tagging using a scroll bar is, for some images, very high. Most of the (most commonly used) linguistic tags are on the basic level (in the sense of Rosch's basic level theory). The distributions of the linguistic tags in our examples follow an inverse power-law. Hence, it seems possible to apply collective image emotion tagging to image information systems and to present a new search option for basic emotions. This article is one of the first steps in the research area of emotional information retrieval (EmIR). © 2009 Wiley Periodicals, Inc.