Sentiment Analysis and Machine Learning Classification of COVID-19 Vaccine Tweets: Vaccination in the Shadow of Fear-Trust Dilemma

Abstract

In addition to infecting millions of people and causing hundreds of thousands of deaths, COVID-19 has also caused psychological and economic devastation. Studies on the vaccine, which is considered to be the only way to eliminate this pandemic, have been rapidly completed and more than 10 vaccines have begun to be applied worldwide by 2021. One of the biggest obstacles to the fight against COVID-19 is the hesitation against the vaccine. The fear factor, fed by incomplete and false information spreading rapidly through social media applications such as Twitter, is thought to be the main reason for this hesitation. In this study, the general sentiment against the COVID-19 vaccine is analyzed. For this, in the first week of January 2021, more than 8000 tweets are extracted with R statistical software and Twitter API, and appropriate sentiment analysis methods are applied. On the other hand, accuracy values are obtained by applying Logistic Regression and Naïve Bayes methods, which are effective and widely used supervised machine learning methods, for sentiment classification. Although the results indicate that there is a positive attitude about the vaccine, it is remarkable that the rate of negative sentiments is relatively high (30%). Trust is the dominant sentiment on the positive side, while fear is the dominant sentiment on the negative side. According to the results of the classification methods, accuracy values are close to 90%.