Public Perception Towards Children's COVID-19 Vaccination with Natural Language Processing

Abstract

Abstract In 2019, Coronavirus manifested itself in China and caused numerous deaths. Vaccines developed against COVID-19 are seen as a way to end or mitigate the pandemic. Many debates arose about the vaccination of children through social media. The main target of this study is to present a model that reveals the perception of parents about getting their children vaccinated, extracts the main themes, and determines the emotional changes. With the support of the Octoparse web scraping tool, data was extracted from Twitter when the epidemic turned into a global problem and the discussions about vaccines intensified. Then, using the topic modeling and sentiment analysis techniques under the umbrella of (Natural Language Processing) NLP, main, sub-topics about parents' attitudes were revealed, also vaccine perceptions were detected by performing sentiment analysis. As a result, four topic clusters were determined: “the opinion of the need for the first dose of vaccination according to age”, “the effectiveness of the first dose of vaccine”, “the opinion of the need for vaccination of school-age children”, and “the need for vaccination arising from the protection of unvaccinated children with only mask protection”. With sentiment analysis, it was seen that positive emotions were dominant, and three emotions, namely trust, expectation, and fear, came to the fore. In conclusion, it has been determined that families trust the states and their announcements about getting their children vaccinated, they anticipate new vaccines to be developed, but they are also afraid of the risks that the vaccine will bring to their children.