8393-38156-1-PB.pdf - Proceedings of the Twenty-Eighth AAAI...

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How Do Your Friends on Social Media Disclose Your Emotions? Yang Yang, Jia Jia, Shumei Zhang, Boya Wu, Qicong Chen, Juanzi Li, Chunxiao Xing, Jie Tang Department of Computer Science and Technology, Tsinghua University Tsinghua National Laboratory for Information Science and Technology (TNList) [email protected], { jjia,lijuanzi, xingcx, jietang } @tsinghua.edu.cn Abstract Extracting emotions from images has attracted much in- terest, in particular with the rapid development of social networks. The emotional impact is very important for understanding the intrinsic meanings of images. Despite many studies having been done, most existing methods focus on image content, but ignore the emotion of the user who published the image. One interesting 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 im- age to help extract the emotions? In this paper, we for- mally formalize the problem and propose a novel emo- tion learning method by jointly modeling images posted by social users and comments added by their 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 the other irrelevant ones. Experiments on an open Flickr dataset show that the proposed model can significantly improve (+37.4% by F1) the accuracy for inferring user emotions. More in- terestingly, we found that half of the improvements are due to interactions between 1.0% of the closest friends. Introduction Image is a natural way to express one’s emotions. For exam- ple, people use colorful images to express their happiness, while gloomy images are used to express sadness. With the rapid development of online social networks, e.g., Flickr 1 and instagram 2 , more and more people like to share their daily emotional experiences using these platforms. Our pre- liminary statistics indicate that more than 38% of the im- ages on Flickr are explicitly annotated with either positive or negative emotions. Understanding the emotional impact of social images can benefit many applications, such as im- age retrieval and personalized recommendation. Besides sharing images, in online social networks such as Flickr and Instagram, posting discussions on a shared image is becoming common. For example, on Flickr, when a user publishes an image, on average 7.5 friends will leave com- ments (when users follow each other on Flickr, we say they Copyright c 2014, Association for the Advancement of Artificial Intelligence ( ). All rights reserved. 1 , the largest photo sharing website. 2 , a newly launched free photo sharing website. Figure 1: The general idea of the proposed emotion learning method.
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