Collective Indexing of Emotions in Images. A Study in Emotional Information Retrieval

Collective Indexing of Emotions in Images. A Study in Emotional Information Retrieval

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by Stefanie Schmidt , Wolfgang G. Stock

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Datum	Value	Source
TITLE	Collective Indexing of Emotions in Images. A Study in Emotional Information Retrieval	SVM HeaderParse 0.2
AUTHOR NAME	Stefanie Schmidt	SVM HeaderParse 0.2
AUTHOR AFFIL	Heinrich-Heine-University Düsseldorf, Dept. for Information Science, Universitätsstr. 1, D-40225 Düsseldorf,	SVM HeaderParse 0.2
AUTHOR NAME	Wolfgang G. Stock	SVM HeaderParse 0.2
AUTHOR AFFIL	Heinrich-Heine-University Düsseldorf, Dept. for Information Science, Universitätsstr. 1, D-40225 Düsseldorf,	SVM HeaderParse 0.2
ABSTRACT	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 www.Flickr.com) 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).	SVM HeaderParse 0.2
CITATIONS	96 found	ParsCit 1.0