Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description

Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description Xianwei Guo, Hua Lai, Yan Xiang∗, Zhengtao Yu, Yuxin Huang Faculty of Information Engineering and Automation, Kunming University of Science and Technology / Kunming, China Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology / Kunming, China sharonxiang@126.com Abstract Emotion classification of COVID-19 Chinese microblogs helps analyze the public opinion trig- gered 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 propose an 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 microblog in 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 and category semantics, the model can better understand the emotion of microblogs. Experiments on the COVID-19 Chinese microblog dataset show that our approach outperforms many existing emotion classification methods, including the BERT baseline. 1 Introduction The COVID-19 pandemic is spreading all over the world, and fighting the pandemic is a protracted battle. It is also an important battle to analyze the COVID-19 related data continuously generated on social media and quickly grasp the public opinion that the pandemic may trigger. Public opinion caused by the pandemic will have an important impact on the decision-making of the government and relevant departments. Automatic emotion classification of COVID-19 related data on social media is helpful to assess the risk of public opinion. Common social media platforms at home and abroad include Weibo, Facebook, Twitter, and so on, which are important ways for netizens to express their opinions and emo- tions (Wang et al., 2017). As one of the largest social media platforms in China, Sina Weibo has generated massive amounts of COVID-19 microblog data. Through analysis, we found that the text of the COVID-19 Chinese mi- croblog is short, lacks context, and has nonstandard expressions. For example, the two posts ‘武汉 加油，中国必赢 (Come on Wuhan, China will win)’ and ‘#新型冠状病毒# 比恐慌更可怕的是怠 慢。—《看见》（讲述非典时所写到） (#Novel Coronavirus# What is more terrifying than panic is neglect. –‘Seeing’ (as written about SARS))’. Generally, existing emotion classification methods only utilize the features of the microblog itself for modeling and do not consider the semantics of emotion cat- egories well. Therefore, they cannot analyze the emotion of nonstandard COVID-19 Chinese microblog text well. To solve this problem, we present an emotion classification method based on the emotion category description. Based on the idea of question answering, the semantic information of …

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