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 largescale 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.
REF consider the "bag-ofvisual words" representation as well as the color distribution of images, and make use of the SentiWordNet thesaurus to extract numerical values for image sentiment from associated textual metadata.
Analyzing and predicting sentiment of images on the social web
ACM Multimedia
Computer Science