Evolution of Public Sentiments During COVID-19 Pandemic
Bian Xiaohui1(),Xu Tong2
1School of Management, Anhui University, Hefei 230039, China 2School of Computer Science, University of Science and Technology of China, Hefei 230027, China
[Objective] This study analyzes the social media posts during the COVID-19 pandemic, aiming to reveal the temporal and spatial differences of public opinion, the sentiment evolution under different circumstances, as well as the trans-regional spreading of the public sentiments. [Methods] Firstly, we utilized the Latent Dirichlet Allocation (LDA) model to generate the latent topics and related keyword groups, which also analyzed public sentiment evolutions from the perspectives of global and individual topics. Then, we described the trans-regional spread of public sentiments based on the social spread model adapted from the classic Independent Cascade Model. [Results] The new model summarized the general rules of the temporal evolution and spatial difference, as well as the impacts of distance to the epidemic centers and the financial levels. We also found two different types of topics indicating reasons for popularity and sentiment differences, as well as multi-view connections among these topics. The strength of trans-regional sentiment spread could be affected by both regional distance and epidemic situation. [Limitations] The new framework could not process the multimodal data. [Conclusions] The proposed model helps the local government make better strategies according to specific conditions, and pay more attention to the impacts of related events. They should also strengthen regional cooperation and coordination for controlling pandemics and monitoring public sentiments.
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