The paper “Analyzing and Predicting Sentiment of Images on the Social Web” co-written by Siersdorfer, Hare, Minack and Deng has been presented at ECML PKDD 2010 in Barcelona, Spain on September 20th-24th, 2010
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.
The LK project is funded by the European Commission under Project No. 231126