Social media analysis tools have been used to monitor public sentiment and communication methods during public health emergencies.
Public health emergencies are required to better understand the impact of the crisis on the public and to provide reference material for the prevention of future public health emergencies. We are concentrating on the sentiments around the public health emergency created by COVID-19.
This study aims to better understand the impact of public health emergencies on citizens and provide reference material for future public health emergency prevention.
The Fuzzy-c-means method was used to divide the 850,083 content of Weibo from January 24, 2020, to March 31, 2020, into seven categories of emotions: fear, happiness, disgust, surprise, sadness, anger, and good. The changes in emotion were tracked over time.
The results indicated that people showed "surprise" overall (55.89%); however with time, the "surprise" decreased. As the knowledge regarding the coronavirus disease 2019 (COVID-19) increased (contents about COVID-19 knowledge: from 21.16% to 4.19%), the "surprise" of the citizens decreased (from 59.95% to 46.58%). Citizens' feelings of "fear" and "good" increased as the number of deaths associated with COVID-19 increased ("fear”: from 15.42% to 20.95% "good”: 10.31% to 18.89%). As the infection was suppressed, the feelings of "fear" and "good" diminished ("fear”: from 20.95% to 15.79% "good”: from 18.89% to 8.46%).
In this study, the emotions and changes in emotions of Weibo users were analyzed in chronological order. The results of this study can prepare for future public health emergencies.