Impact of Social Sentiment Changes on People's Consumption Behavior during COVID-19 Outbreak

A better understanding of people's consumption behavior during the COVID-19 pandemic plays a significant role in providing informative decision support for the consumption supply of the overall society and government planning for different resources. This paper proposes to link the social sentiment and people's consumption behaviour, and to understand how the social sentiment changes affect people's consumption behaviour. In this study, Sina Weibo (a Chinese social networking platform where people communicate in short messages) text data is collected to extract the social sentiment situations with machine learning techniques, while Dianping (a Chinese social networking platform where people share consumption experiences) text data is collected to extract people's consumption behaviour with machine learning approaches. The statistical analysis found that there was a significant correlation between social sentiment changes and consumption behavior during the COVID-19 epidemic in the studied city. The research results can be used to provide reasonable suggestions for government departments to make informative decisions and guide the supplies for people's consumption.