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.

In this study, stainless steel bars with the same diameter were used to replace ordinary stressed steel bars in reinforced concrete bridge piers. An ultra-high drop hammer impact test system was adopted for multiple horizontal impact tests on two groups of reinforced concrete bridge pier specimens. Based on the study results, it was determined that the maximum impact force of the stainless steel–reinforced concrete specimen, strain occurred in the steel bar and concrete, and the maximum displacement at the specimen’s top was found to be smaller than that before the same diameter replacement under the same impact energy. These results indicate that the stainless steel–reinforced concrete bridge pier specimen may have strong ductility, energy absorption abilities, and impact resistance under the impact load.

Twitter sentiment analysis enables scientists to monitor people’s attitudes to public health policies and events in the era of COVID-19. Although the pre-trained model can conduct sentiment analysis since the beginning of a pandemic or a global emergency, this model is not optimized for a specific topic. Furthermore, the way people express opinions on the topic may quickly change during the emergency. Therefore, the early-trained model may not work consistently well during the emergency. Unfortunately, the late-trained model will not be available until months after the emergency begins. In this paper, we propose an ensemble method to combine the pre-trained model and early-trained model for achieving better analysis performance on COVID-19-related tweets. The effectiveness of this method has been verified by the experiments.