Emotion Classification of COVID -19 C hinese Microblogs based on the Emotion Category Description (2021)

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.

## 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 emotions @xcite . 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 microblog 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 categories well. therefore, they cannot analyze the emotion of nonstandard 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 categories is fused to help the model understand the emotion of microblogs. in the next paragraph, we will analyze the impact of category semantic information on the emotion classification of covid-19 chinese microblogs. emotion classification requires identifying specific sentiments in the text, such as happiness, anger, sadness, and fear @xcite . traditional supervised emotion classification models generally transform the categories into digital labels, as the supervised signal to guide the learning process of the model. for example, traditional models use '1' for happiness emotion and '2' for anger emotion. normally, the digital label will be represented as a one-hot vector, and be used to calculate the training loss. then the backpropagation algorithm is used to minimize the objective function to train the model. traditional models do not adequately consider the semantic information of emotion categories, which means that they do not better learn the meaning of emotion categories and cannot accurately classify microblogs into corresponding emotion categories. when judging the emotions expressed in a text, humans usually have some prior knowledge. for example, the microblog '这人 什么心态！能不能对自己和他人都负点责，丈夫确诊隔离了，妻子还往外跑，还坐火车，什么 心态 (what mentality of this person! can you take responsibility for yourself and others? the husband is diagnosed and isolated, and the wife is still going out and taking the train. what mentality?)'. when humans are judging the emotion of this microblog, the embodiment of prior knowledge is that they know the specific meaning of anger emotion, that is, someone is agitated because of extreme dissatisfaction, so it is easy to correctly judge the emotion of this microblog. however, the models do not have prior knowledge, so they are not ideal for microblog emotion learning. in other words, if the models can grasp the prior knowledge that humans have, they will better understand the emotion of the text. by asking what the emotion of a certain microblog expresses and then giving an answer, this process of judging the emotions of the text is similar to the question-answering task. inspired by this, we introduce the idea of question answering into the emotion classification task of covid-19 chinese microblogs. the main contributions are summarized as follows: ## related work emotion classification of covid-19 chinese microblogs is essentially a sentiment classification task. recently, with the rise of deep learning, existing sentiment classification studies are usually based on deep learning methods. neural network models, such as recurrent neural networks (rnns), convolutional neural networks (cnns), and transformers, have been proven effective in many sentiment classification tasks. @xcite first used cnn or lstm to encode a single sentence and then used gated rnn to encode the internal relations and semantic connections between sentences. finally, they obtained the representation of the document to complete sentiment classification. @xcite proposed a context-aware bidirectional lstm model, which used forward and backward lstms to jointly encode the context information of the text. it has achieved good results in the emotion classification of chinese microblogs. @xcite proposed for the first time to use convolutional neural networks to extract text sequence features for sentence-level text classification and completed sentiment classification on movie review datasets. since then, a series of sentiment classification methods based on cnn have been produced @xcite . @xcite proposed a word-level dpcnn, which extracted long-distance text dependencies by continuously deepening the network. they performed sentiment classification on review datasets such as amazon and achieved the best results at c c l 2 0 2 1 the time. @xcite completed the enhancement of emotional semantics by mapping the commonly used emoji vector representation and the word vector representation of the text to the same emotional space. then they used a multi-channel convolutional neural network to classify the emotion of chinese microblogs. in the above research, although the rnn-based model can effectively process serialized text, it also has the problem of sequence dependence and cannot be calculated in parallel. although the cnn-based model can be processed in parallel, its ability to capture long-distance features is weak due to its mechanism of extracting text features through sliding convolution windows. besides, cnn-based models generally use a pooling layer to integrate text features, but this will lose the location information of the text, which is another serious problem. to improve these problems, @xcite proposed the transformer model, which completely took the self-attention mechanism as the basic structure of the model, and abandoned the loop structure of rnn and the convolution structure of cnn. at the same time, transformer not only has all the advantages of rnn and cnn but also solves the problem of sequence dependence of rnn and the problem of cnn's weak ability to capture long-distance features. devlin et al. (2019) proposed the bert model based on transformer, which opened the prelude to the development of pre-training language models and refreshed the records of a series of nlp tasks including sentiment classification tasks. since then, a series of transformer-based models have been proposed, which can be referred to as transformers @xcite . to sum up, the current methods of sentiment classification mainly focus on neural network models such as rnns, cnns, and transformers. these methods only perform modeling based on the text semantics and fail to utilize the semantic information of classification categories. studies have shown that the semantic information of categories is effective for classification problems. for example, @xcite integrated the semantics of the category into the model in the form of word embedding, which improved the performance of the text classification task. @xcite guided the learning process of the model by using all the category descriptions as questions and the classified text as the answer, thereby enhancing the performance of the text classification task. their method requires the model to ask about the emotion of the text n times, where n is the number of categories. this process seems not easy to understand, that is, when humans judge the category of a text, they usually understand the semantics of its category, and then combine their knowledge to make judgments. different from @xcite , we construct a question for the input microblog text, and then use all category descriptions as candidate answer set to classify the emotion of the text by a question answering (qa) based method. by constructing a question-and-answer pair to combine each microblog with category descriptions, the model can focus on both the category information related to the microblog and the microblog information related to categories. we also introduce the attention mechanism to focus on the important information in the candidate answer set. emotion classification requires not only understanding the semantics of the text, but also the emotions contained in the text. therefore, how to accurately abstract and integrate the semantic information of emotion categories to help the model better understand the emotions of the text is an important issue that we focus on. ## methods in this section, we present our emotion classification method for covid-19 chinese microblogs, which contains two parts: (1) the definition and strategy of emotion category description (section 3.1), and (2) the emotion classification fine-tuning based on a question answering (qa) method (section 3.2). ## conclusion this paper proposes an emotion classification method based on the emotion category description for covid-19 chinese microblog data. by extending the emotion category into a formalized category description, the semantic information of the category is integrated to guide the model to classify emotions. experimental results show that our method can effectively model the nonstandard covid-19 microblog text, and the introduced category description semantic information helps the model understand the semantics and emotions of the irregular text. it also proves that introducing the idea of question answering into the bert model can significantly improve the performance of the covid-19 microblog emotion classification. moreover, the issue of category imbalance in emotion classification is a challenge for existing studies. in the future, we will further investigate the category imbalance of covid-19 microblogs.