Analysis of Community Sentiment on Twitter towards COVID-19 Vaccine Booster Using Ensemble Bagging Methods

Abstract

COVID-19 is an infectious disease caused by a newly discovered type of coronavirus. Based on recommendations from the Technical Advisory Group on Virus Evolution, WHO established a new variant called Omicron. Due to the rapid spread of COVID-19, a booster vaccine was created to deal with the new virus variant. However, the strategy of giving vaccines that never ends is considered controversial by the community, and this is shown by the number of people who express their opinions, both positive and negative opinions on social media, one of which is Twitter. This research was conducted by collecting data with the help of the Twitter API. The classification method uses ensemble bagging with three basic lessons, namely Naive Bayes, K-Nearest Neighbor, and Decision Tree. Meanwhile, the feature extraction used in this research is TF-IDF (Term Frequency-Inverse Document Frequency). The performance of the ensemble bagging method by applying Hyperparameter Tuning is a precision of 0.72, recall of 0.71, F1-Score of 0.72, and accuracy of 0.72.