@inproceedings{xianwei-etal-2021-emotion,
title = "Emotion Classification of {COVID}-19 {C}hinese Microblogs based on the Emotion Category Description",
author = "Xianwei, Guo and
Hua, Lai and
Yan, Xiang and
Zhengtao, Yu and
Yuxin, Huang",
editor = "Li, Sheng and
Sun, Maosong and
Liu, Yang and
Wu, Hua and
Liu, Kang and
Che, Wanxiang and
He, Shizhu and
Rao, Gaoqi",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.82",
pages = "916--927",
abstract = "Emotion classification of COVID-19 Chinese microblogs helps analyze the public opinion triggered by COVID-19. Existing methods only consider the features of the microblog itself with-out combining the semantics of emotion categories for modeling. Emotion classification of mi-croblogs is a process of reading the content of microblogs and combining the semantics of emo-tion categories to understand whether it contains a certain emotion. Inspired by this we proposean emotion classification model based on the emotion category description for COVID-19 Chi-nese microblogs. Firstly we expand all emotion categories into formalized category descriptions. Secondly based on the idea of question answering we construct a question for each microblogin the form of {`}What is the emotion expressed in the text X?{'} and regard all category descrip-tions as candidate answers. Finally we construct a question-and-answer pair and use it as the input of the BERT model to complete emotion classification. By integrating rich contextual andcategory semantics the model can better understand the emotion of microblogs. Experimentson the COVID-19 Chinese microblog dataset show that our approach outperforms many existinge motion classification methods including the BERT baseline.",
language = "English",
}
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<abstract>Emotion classification of COVID-19 Chinese microblogs helps analyze the public opinion triggered by COVID-19. Existing methods only consider the features of the microblog itself with-out combining the semantics of emotion categories for modeling. Emotion classification of mi-croblogs is a process of reading the content of microblogs and combining the semantics of emo-tion categories to understand whether it contains a certain emotion. Inspired by this we proposean emotion classification model based on the emotion category description for COVID-19 Chi-nese microblogs. Firstly we expand all emotion categories into formalized category descriptions. Secondly based on the idea of question answering we construct a question for each microblogin the form of ‘What is the emotion expressed in the text X?’ and regard all category descrip-tions as candidate answers. Finally we construct a question-and-answer pair and use it as the input of the BERT model to complete emotion classification. By integrating rich contextual andcategory semantics the model can better understand the emotion of microblogs. Experimentson the COVID-19 Chinese microblog dataset show that our approach outperforms many existinge motion classification methods including the BERT baseline.</abstract>
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%0 Conference Proceedings
%T Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description
%A Xianwei, Guo
%A Hua, Lai
%A Yan, Xiang
%A Zhengtao, Yu
%A Yuxin, Huang
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G English
%F xianwei-etal-2021-emotion
%X Emotion classification of COVID-19 Chinese microblogs helps analyze the public opinion triggered by COVID-19. Existing methods only consider the features of the microblog itself with-out combining the semantics of emotion categories for modeling. Emotion classification of mi-croblogs is a process of reading the content of microblogs and combining the semantics of emo-tion categories to understand whether it contains a certain emotion. Inspired by this we proposean emotion classification model based on the emotion category description for COVID-19 Chi-nese microblogs. Firstly we expand all emotion categories into formalized category descriptions. Secondly based on the idea of question answering we construct a question for each microblogin the form of ‘What is the emotion expressed in the text X?’ and regard all category descrip-tions as candidate answers. Finally we construct a question-and-answer pair and use it as the input of the BERT model to complete emotion classification. By integrating rich contextual andcategory semantics the model can better understand the emotion of microblogs. Experimentson the COVID-19 Chinese microblog dataset show that our approach outperforms many existinge motion classification methods including the BERT baseline.
%U https://aclanthology.org/2021.ccl-1.82
%P 916-927
Markdown (Informal)
[Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description](https://aclanthology.org/2021.ccl-1.82) (Xianwei et al., CCL 2021)
ACL