Predicting Viewer Perceived Emotions In Animated Gifs

Animated GIFs are everywhere on the Web. Our work focuses on the computational prediction of emotions perceived by viewers after they are shown animated GIF images. We evaluate our results on a dataset of over 3,800 animated GIN gathered from MIT's GIFGIF platform, each with scores for 17 discrete emotions aggregated from over 2.5M user annotations - the first computational evaluation of its kind for content-based prediction on animated GIFs to our knowledge. In addition, we advocate a conceptual paradigm in emotion prediction that shows delineating distinct types of emotion is important and is useful to be concrete about the emotion target. One of our objectives is to systematically compare different types of content features for emotion prediction, including low-level, aesthetics, semantic and face features. We also formulate a multi-task regression problem to evaluate whether viewer perceived emotion prediction can benefit from jointly learning across emotion classes compared to disjoint, independent learning.