Abstract
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.
Highlights
A manual analysis showed that these peaks were strongly related to the stay-at-home suggestion given by the Centers for Disease Control and Prevention (CDC)
We proposed two data preprocessing modes, which are the close mode and the complete mode, for improving the performances of early trained models by balancing the training data set. Both modes achieved a significant performance improvement and outperformed the original early trained models. Both modes have shown an evident advantage in the computation of overall sentiment scores, which is evaluated by average sentimental score error (ASSE)
This advantage meets the need of policy surveillance systems, which focus on overall sentiment changes
Summary
Public health scientists have developed many approaches in this field [5,6,7,8] These methods use evidence from multiple sources, such as medical literature, clinically gathered information, health information databases, and survey results. Data collection is required to fit the time frame of the relevant and related policies [12,13]. Policy surveillance systems need to monitor patterns and trends of the related policy influence [14,15]. These systems require the data to be collected efficiently and feasibly. It is difficult to completely meet these requirements using traditional approaches, e.g., key informant interviews and case studies [3], due to the time delay caused by the inefficient processing steps of most of these approaches [16]
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