How do your friends on social media disclose your emotions

Mining emotions hidden in images has attracted signif-icant interest, in particular with the rapid development of social networks. The emotional impact is very impor-tant for understanding the intrinsic meanings of images. Despite many studies have been done, most existing methods focus on image content, but ignore the emo-tions of the user who has published the image. To un-derstand the emotional impact from images, one inter-esting question is: How does social effect correlate with the emotion expressed in an image? Specifically, can we leverage friends interactions (e.g., discussions) related to an image to help discover the emotions? In this paper, we formally formalize the problem and propose a novel emotion learning method by jointly modeling images posted by social users and comments added by friends. One advantage of the model is that it can distinguish those comments that are closely related to the emotion expression for an image from other irrelevant ones. Ex-periments on an open Flickr dataset show that the pro-posed model can significantly improve (+37.4 % by F1) the accuracy for inferring user emotions. More interest-ingly, we found that half of the improvements are due to interactions between 1 % of the closest friends