An improved BERT method for the evolution of network public opinion of major infectious diseases: Case Study of COVID-19

Infectious diseases can cause a sudden and serious spread of public opinion, making it a popular topic for analysis. However, current research on public sentiment mostly relies on traditional machine learning methods, which are limited by sample size and labor costs. This paper presents a novel deep learning model called PCABERT, an improved BERT model that utilizes principal component analysis (PCA) to extract and fuse the effective features of each layer of the BERT model. This approach offers a more accurate measurement of public sentiment. Furthermore, this paper proposes an analytical framework to comprehensively study the characteristics of network public opinion evolution in major infectious diseases from three perspectives: content, structure, and behavior. To validate the proposed model and framework, we analyze the COVID-19 pandemic as a case study and collect social media data from the past three years since the outbreak. We calculate public emotions using the PCA-BERT model and combine the obtained emotional values to summarize the temporal and spatial laws of the evolution of network public opinion in terms of content, structure, and behavior. This study can help guide the government to identify public demands during the epidemic and carry out epidemic prevention and control more effectively.