Sentiment Analysis Method of Epidemic-related Microblog Based on Hesitation Theory

Abstract

The COVID-19 pandemic in 2020 brought an unprecedented global crisis. After two years of control efforts, life gradually returned to the pre-pandemic state, but localized outbreaks continued to occur. Towards the end of 2022, COVID-19 resurged in China, leading to another disruption of people’s lives and work. Many pieces of information on social media reflected people’s views and emotions towards the second outbreak, which showed distinct differences compared to the first outbreak in 2020. To explore people’s emotional attitudes towards the pandemic at different stages and the underlying reasons, this study collected microblog data from November 2022 to January 2023 and from January to June 2020, encompassing Chinese reactions to the COVID-19 pandemic. Based on hesitancy and the Fuzzy Intuition theory, we proposed a hypothesis: hesitancy can be integrated into machine learning models to select suitable corpora for training, which not only improves accuracy but also enhances model efficiency. Based on this hypothesis, we designed a hesitancy-integrated model. The experimental results demonstrated the model’s positive performance on a self-constructed database. By applying this model to analyze people’s attitudes towards the pandemic, we obtained their sentiments in different months. We found that the most negative emotions appeared at the beginning of the pandemic, followed by emotional fluctuations influenced by social events, ultimately showing an overall positive trend. Combining word cloud techniques and the Latent Dirichlet Allocation (LDA) model effectively helped explore the reasons behind the changes in pandemic attitude.