Sentiment Analysis About COVID-19 Booster Vaccine on Twitter Using Deep Learning

Abstract

The rapid spread of COVID-19 cases to various countries has made the COVID-19 outbreak a global pandemic by the World Health Organization (WHO). The effect of the designation of COVID-19 as a pandemic has prompted the government to take preventive action against vaccination, as well as the WHO which has asked the public to immediately get a third or booster dose of vaccine. Various responses regarding the COVID-19 booster vaccine continue to emerge on social media such as Twitter. Twitter is often used by its users to express emotions about something either positive or negative. People tend to believe what they find on social networks, which makes them vulnerable to rumors and fake news. Sentiment analysis or opinion mining is one solution to overcome the problem of automatically classifying opinions or reviews into positive or negative opinions. In this study, the Deep Learning algorithm was used to analyze public opinion sentiment regarding the COVID-19 booster vaccine on Twitter. The data collection method used is crawling data using an access token obtained from the Twitter API. Meanwhile, to evaluate the model, the K-fold Cross-Validation method is used. The results of testing the model obtained the highest accuracy value at iterations = 10, which is 82.78% with AUC value = 0.836, precision = 83.33% and recall = 95.89%.