Impact of COVID-19 on mental health in China: analysis based on sentiment knowledge enhanced pre-training and XGBoost algorithm - Huang, Ru; Wang, Xiuli (Frontiers in Public Health, 2023)
Coronavirus disease 2019 (COVID-19) is causing a serious impact on the people living in countries across the entire world. The spread of this pandemic globally has led people worry every day about losing their jobs or even being threatened by the virus. This pandemic caused people to experience more serious psychological problems than we realized. However, there has been little research on how COVID-19 affects the mental health of the people. In this article, we attempted to use the social text data about COVID-19 on Sina Weibo (the largest “tweet” platform in China, and we will also call Weibo as tweet in the following content), to explore the impact of COVID-19 on the mental health of Chinese people. First, we fifilter the tweet data by selecting examples that contain COVID-19 and COVID-19 correlated keywords. However, we segment the filtered tweets, extract meaningful words, and construct a word vector sparse matrix as the measurement of every tweet. Then, for the model's labels, we use sentiment knowledge enhanced pre-training model (SKEP), a deep learning framework published by Baidu that measures the user's mental state. Through SKEP, we can obtain the probabilities of the user's positive and negative mental states. Finally, we use the XGBoost algorithm to study the relationship between the word vector sparse matrix and the mental health state of users. Our research shows that social text data can, indeed, reflect the mental health state of users to a large extent, and social data can be used to explore the impact of COVID-19 on mental health, which can help frame the public health policy.