Neural Emotion Detection via Personal Attributes

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 t...

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Bibliographic Details
Published in:Journal of computer science and technology 2022-10, Vol.37 (5), p.1146-1160
Main Authors: Zhou, Xia-Bing, Wang, Zhong-Qing, Liang, Xing-Wei, Zhang, Min, Zhou, Guo-Dong
Format: Article
Language:English
Subjects:
Analysis
Artificial Intelligence
Computational linguistics
Computer Science
Data Structures and Information Theory
Digital media
Emotion recognition
Emotions
Facial recognition technology
Information Systems Applications (incl.Internet)
Language processing
Natural language interfaces
Regular Paper
Social media
Social networks
Software Engineering
Theory of Computation
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Summary: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.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-021-0606-7