Policymakers and relevant public health authorities can analyze people’s attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people’s attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people’s attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people’s attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.

Background: In the era of a pandemic like COVID-19, monitoring the sentimental changes of the population is an urgent need, especially for the policy makers of the public health. A possible solution is to build a fast and low-cost surveillance system by using the sentiment analysis of Twitter data. Unfortunately, choosing a suitable sentiment classification model is still challenging. The general pre-trained model may be insensitive to the new specific terms of the pandemic. The early-trained model may have a bias issue due to the incomplete specific corpus. Although it is reasonable to assume the late-trained model is relatively reliable, it is usually available months after a pandemic begins. Methods: This paper conducts the sentiment analysis of Twitter data and compares different models. Furthermore, we propose a strategy for using the pre-trained, early-trained, and latetrained models in a surveillance system based on Twitter data. The first two models can be used together in the early stage, while the last model can be used in the late stage. This study also analyzes the relationship between the sentimental changes of COVID-19-related Twitter data and the public health policies and events. Results: Our results indicate that applying the pre-trained model to preprocessing early training samples may improve the early-trained model. Both models can work together by making up each other in the surveillance system in the early stage. Conclusions: A fast and low-cost surveillance system is critical to the policy makers of the public health in a pandemic. This work uses the sentiment analysis of Twitter data to evaluate people’s attitudes to public health policies and events. We propose a strategy to make the surveillance system effective since the early stage. This study also connects the sentimental changes of COVID19-related Twitter data to the public health policies and events.