Journal of Computer Science and Technology ›› 2022, Vol. 37 ›› Issue (5): 1146-1160.doi: 10.1007/s11390-021-0606-7

Special Issue: Artificial Intelligence and Pattern Recognition

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

Neural Emotion Detection via Personal Attributes

Xia-Bing Zhou1 (周夏冰), Member, CCF, Zhong-Qing Wang1,* (王中卿), Member, CCF, Xing-Wei Liang2 (梁兴伟), Min Zhang1 (张民), Member, CCF, and Guo-Dong Zhou1 (周国栋), Distinguished Member, CCF, Member, ACM, IEEE         

  1. 1School of Computer Science and Technology, Soochow University, Suzhou 215006, China
    2Konka AIOT Research Laboratory, Konka Group Company Limited, Shenzhen 518053, China
  • Received:2020-05-08 Revised:2021-04-01 Accepted:2021-04-13 Online:2022-09-30 Published:2022-09-30
  • Contact: Zhong-Qing Wang E-mail:wangzq.antony@gmail.com
  • About author:Zhong-Qing Wang received his Ph.D. degree in computer science and technology in 2016 from Soochow University, Suzhou. He is currently an associated professor in the School of Computer Science and Technology, Soochow University, Suzhou. His research interests include natural language processing, sentiment analysis, and information retrieval. Prior to joining Soochow University, he was a research associate at the Hong Kong Polytechnic University (2014-2015), Hong Kong, and a postdoctoral research fellow at Singapore University of Technology Design (2016-2017), Singapore.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant Nos. 62176174 and 61806137.

There has been a recent line of work to automatically detect the emotions of posts in social media. In literature, studies treat posts independently and detect their emotions separately. Different from previous studies, we explore the dependence among relevant posts via authors' backgrounds, since the authors with similar backgrounds, e.g., "gender", "location", tend to express similar emotions. However, personal attributes are not easy to obtain in most social media websites. Accordingly, we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection: the Joint Model with Personal Attention Mechanism (JPA) model is used to detect emotion and personal attributes jointly, and capture the attributes-aware words to connect similar people; the Neural Personal Discrimination (NPD) model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection. Experimental results show the usefulness of personal attributes in emotion detection, and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.

Key words: emotion detection; adversarial network; attention mechanism; personal attribute;

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