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2014 IEEE International Conference on Multimedia and Expo (ICME) (2014)
Chengdu, China
July 14, 2014 to July 18, 2014
ISBN: 978-1-4799-4761-4
pp: 1-6
Huijie Lin , Department of Computer Science and Technology, Tsinghua University, Beijing, China
Jia Jia , Department of Computer Science and Technology, Tsinghua University, Beijing, China
Quan Guo , Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
Yuanyuan Xue , Department of Computer Science and Technology, Tsinghua University, Beijing, China
Jie Huang , Department of Computer Science and Technology, Tsinghua University, Beijing, China
Lianhong Cai , Department of Computer Science and Technology, Tsinghua University, Beijing, China
Ling Feng , Department of Computer Science and Technology, Tsinghua University, Beijing, China
ABSTRACT
Long-term stress may lead to many severe physical and mental problems. Traditional psychological stress detection usually relies on the active individual participation, which makes the detection labor-consuming, time-costing and hysteretic. With the rapid development of social networks, people become more and more willing to share moods via microblog platforms. In this paper, we propose an automatic stress detection method from cross-media microblog data. We construct a three-level framework to formulate the problem. We first obtain a set of low-level features from the tweets. Then we define and extract middle-level representations based on psychological and art theories: linguistic attributes from tweets' texts, visual attributes from tweets' images, and social attributes from tweets' comments, retweets and favorites. Finally, a Deep Sparse Neural Network is designed to learn the stress categories incorporating the cross-media attributes. Experiment results show that the proposed method is effective and efficient on detecting psychological stress from microblog data.
INDEX TERMS
Stress, Psychology, Feature extraction, Image color analysis, Pragmatics, Brightness, Joints
CITATION

H. Lin et al., "Psychological stress detection from cross-media microblog data using Deep Sparse Neural Network," 2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu, China, 2014, pp. 1-6.
doi:10.1109/ICME.2014.6890213
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