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A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Ranking and classifying attractiveness of photos in folksonomies
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Collective indexing of emotions in images. A study in emotional information retrieval
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Emotion based classification of natural images
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Towards social imagematics: sentiment analysis in social multimedia
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WebChild: harvesting and organizing commonsense knowledge from the web
Proceedings of the 7th ACM international conference on Web search and data mining
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In this paper we study the connection between sentiment of images expressed in metadata and their visual content in the social photo sharing environment Flickr. To this end, we consider the bag-of-visual words representation as well as the color distribution of images, and make use of the SentiWordNet thesaurus to extract numerical values for their sentiment from accompanying textual metadata. We then perform a discriminative feature analysis based on information theoretic methods, and apply machine learning techniques to predict the sentiment of images. Our large-scale empirical study on a set of over half a million Flickr images shows a considerable correlation between sentiment and visual features, and promising results towards estimating the polarity of sentiment in images.