Image and Collateral Text in Support of Auto-annotation and Sentiment Analysis

We present a brief overview of the way in which image analysis, coupled with associated collateral text, is being used for auto-annotation and sentiment analysis. In particular, we describe our approach to auto-annotation using the graph- theoretic dominant set clustering algorithm and the annotation of images with sentiment scores from SentiWordNet. Preliminary results are given for both, and our planned work aims to explore synergies between the two approaches.