A Study on Public Sentiment of Comments from COVID-19-related Videos: Research Track on Social Network Analysis, Social Media, & Mining (CSCI-RTNA)

To provide decision-making references for the optimization of media release strategies and the correct guidance of public sentiment in the era of COVID-19, this paper selects a series of videos created by “Lin Chen Hearing”, a We Media practitioner who attracted significant attention during the lockdown of Wuhan, China. We establish a sample dataset for video communication and public comments and use the natural language processing technology to build a sentiment classification model. We analyze the impact of video information on public sentiment, hoping to provide s reference for the formulation of media release strategies, agenda setting and public opinion control in the era of COVID-19.