Inferring Emotional Tags From Social Images With User Demographics
Boya Wu, Jia Jia, Yang Yang, Peijun Zhao, Jie Tang, Qi Tian · IEEE Transactions on Multimedia · 2017
Social images, which are images uploaded and shared on social networks, are used to express users’ emotions. Inferring emotional tags from social images is of great importance; it can benefit many applications, such as image retrieval and recommendation. Whereas previous related research has primarily focused on exploring image visual features, we aim to address this problem by studying whether user demographics make a difference regarding users’ emotional tags of social images. We first consider how to model the emotions of social images. Then, we investigate how user demographics, such as gender, marital status, and occupation, are related to the emotional tags of social images. A partially labeled factor graph model named the demographics factor graph model ( D-FGM ) is proposed to leverage the uncovered patterns. Experiments on a data set collected from the world's largest image sharing website Flickr 1 1 [Online]. Available: http://www.flickr.com/ confirm the accuracy of the proposed model. We also find some interesting phenomena. For example, men and women have different patterns to tag “anger” for social images.