Modeling Emotion Influence from Images in Social Networks
This work addresses emotion analysis in social networks for applications like sentiment prediction, but it is incremental as it builds on existing factor graph methods.
The paper tackled the problem of modeling how emotions are embedded in images on social networks and how social influence changes users' emotions, showing that their model improves emotion prediction accuracy by 5% on Flickr data.
Images become an important and prevalent way to express users' activities, opinions and emotions. In a social network, individual emotions may be influenced by others, in particular by close friends. We focus on understanding how users embed emotions into the images they uploaded to the social websites and how social influence plays a role in changing users' emotions. We first verify the existence of emotion influence in the image networks, and then propose a probabilistic factor graph based emotion influence model to answer the questions of "who influences whom". Employing a real network from Flickr as experimental data, we study the effectiveness of factors in the proposed model with in-depth data analysis. Our experiments also show that our model, by incorporating the emotion influence, can significantly improve the accuracy (+5%) for predicting emotions from images. Finally, a case study is used as the anecdotal evidence to further demonstrate the effectiveness of the proposed model.