MenuSkip to content我 | About啰嗦 | Blah blah呓语 | Dream Talk生息 | Life Stream习明纳尔 | Seminar  Blah BlahGUO Quan not blah blah every day.    我 | About郭泉是中国四川大学计算机学院机器智能实验室的博士生，导师是章毅教授。郭泉分别于2013年和2010年在四川大学获得了理学硕士和工学学士学位。 请查看简历，领英页面，以及谷歌学术页面。Quan Guo is currently a Ph.D. student in Machine Intelligence Lab at College of Computer Science, Sichuan University, China, supervised by Professor Zhang Yi. He also received his Master's degree and Bachelor's degree from Sichuan University in 2013 and 2010 respectively.Check the CV, the LinkedIn profile, and the Google Scholar page.    研究兴趣 | Research Interest机器智能 | Machine Intelligence认知科学 | Cognitive Science神经科学 | Neuroscience神经网络 | Neural Networks回复式神经网络 | Recurrent Neural Networks深度学习 | Deep Learning    近期论文 | Recent Publications2014 章毅, 郭泉, 张蕾, and 吕建成, "深度网络和认知计算," 中国计算机学会通讯, vol. 10, iss. 2, pp. 26-32, 2014.  [BibTeX] [Download PDF]@ARTICLE{yi2014cccf,author={章毅 and 郭泉 and 张蕾 and 吕建成},journal={中国计算机学会通讯},title={深度网络和认知计算},year={2014},volume={10},number={2},
pages={26--32},
url={http://www.ccf.org.cn/sites/ccf/zlcontnry.jsp?contentId=2785161647337},
} H. Lin, J. Jia, Q. Guo, Y. Xue, Q. Li, J. Huang, L. Cai, and L. Feng, "User-level psychological stress detection from social media using deep neural network," in Proceedings of the acm international conference on multimedia, New York, NY, USA,  2014, pp. 507-516. doi:10.1145/2647868.2654945  [BibTeX] [Abstract] [Download PDF]It is of significant importance to detect and manage stress before it turns into severe problems. However, existing stress detection methods usually rely on psychological scales or physiological devices, making the detection complicated and costly. In this paper, we explore to automatically detect individuals' psychological stress via social media. Employing real online micro-blog data, we first investigate the correlations between users' stress and their tweeting content, social engagement and behavior patterns. Then we define two types of stress-related attributes: 1) low-level content attributes from a single tweet, including text, images and social interactions; 2) user-scope statistical attributes through their weekly micro-blog postings, leveraging information of tweeting time, tweeting types and linguistic styles. To combine content attributes with statistical attributes, we further design a convolutional neural network (CNN) with cross autoencoders to generate user-scope content attributes from low-level content attributes. Finally, we propose a deep neural network (DNN) model to incorporate the two types of user-scope attributes to detect users' psychological stress. We test the trained model on four different datasets from major micro-blog platforms including Sina Weibo, Tencent Weibo and Twitter. Experimental results show that the proposed model is effective and efficient on detecting psychological stress from micro-blog data. We believe our model would be useful in developing stress detection tools for mental health agencies and