Abstract
Unexpected but exceedingly consequential, the COVID-19 outbreak has undermined livelihoods, disrupted the economy, induced upheavals, and posed challenges to government decision-makers. Under various behavioural regulations, such as social distancing and transport limitations, social media has become the central platform on which people from all regions, regardless of local COVID-19 severity, share their feelings and exchange thoughts. Our study illustrates the evolution of moods expressed on social media regarding COVID-19-related issues and empirically confirms the hypothesis that the severity of the pandemic substantially correlates with these sentiments by analysing tweets on Sina Weibo (China's central social media platform). Methodologically, we leveraged Sentiment Knowledge Enhanced Pre-training, the most state-of-the-art natural language processing pre-trained sentiment-related multipurpose model, to label Sina Weibo tweets during the most distressed period in 2020. Given that the model itself does not provide a feature explanation, we utilize a random forest and linear probit model with the labelled sample to demonstrate how each word plays a role in the prediction. Finally, we demonstrate a strong negative linear relationship between the local severity of COVID-19 and the local sentiment response by incorporating miscellaneous geo-economic control variables. In short, our study reveals how pandemics affect local sentiment and, in a broader sense, provides an easy-to-implement and explanatory pipeline to classify sentiments and resolve related socioeconomic issues.