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典型文献
Neural Emotion Detection via Personal Attributes
文献摘要:
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.
文献关键词:
作者姓名:
Xia-Bing Zhou;Zhong-Qing Wang;Xing-Wei Liang;Min Zhang;Guo-Dong Zhou
作者机构:
School of Computer Science and Technology,Soochow University,Suzhou 215006,China;Konka AIOT Research Laboratory,Konka Group Company Limited,Shenzhen 518053,China
引用格式:
[1]Xia-Bing Zhou;Zhong-Qing Wang;Xing-Wei Liang;Min Zhang;Guo-Dong Zhou-.Neural Emotion Detection via Personal Attributes)[J].计算机科学技术学报(英文版),2022(05):1146-1160
A类:
B类:
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AB值:
0.50092
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