Unveiling Sentiments and Topics in COVID-19 Vaccine Comments on YouTube Over Time: from the First Vaccine Approval to the Post-Pandemic Era

The project was part of the Text and Multimedia Mining course of the first semester in the Artificial Intelligence master program, Radboud University, Nijmegen, Netherlands

While extensive research has examined emotional responses, public sentiments, and topic modeling related to COVID-19 vaccines, the majority of them have primarily utilized data from the early breakout of coronavirus or vaccine rollout until 2021 or 2022. However, a deeper insight into the public’s opinions can be gained by taking into account discourses on YouTube videos related to the coronavirus vaccines from the first vaccine approval until the post-pandemic era. The paper analyses a dataset that consists of YouTube comments related to COVID-19 vaccine with a focus on comments published from August 11, 2020, when it was the world’s first COVID-19 vaccine approval from Russia to December 2023, which is seven months beyond the official declaration that COVID-19 is no longer considered a global threat (May 2023). Through BERT language models, the paper presents a monthly sentiment analysis and topic modeling, offering insights into how discussions evolved as vaccine development extended and the pandemic progressed. The analysis highlights persistent negative sentiments, especially around vaccine efficacy and safety, while also capturing moments of positive sentiment reflecting support for healthcare efforts and vaccine developments. This extended timeframe allows for a subtle insight into the public discourse through the various stages of the pandemic’s impact.