Sentiment Analysis of COVID-19 Booster Vaccines on Twitter Using Multi-Class Support Vector Machine

As part of its response to combat COVID-19, the Indonesian government has implemented a booster vaccination program. This policy has sparked various public responses, particularly across the Twitter platform. This research examines public sentiment regarding booster vaccines by analysing Twitter data through the Support Vector Machine (SVM) algorithm. The research utilises sentiment analysis, a text mining and processing technique, to represent and interpret text-based data. By examining the sentiment expressed in tweets, the study seeks better to understand the public discourse on booster shots on Twitter. The study also conducts a multi-class parameter assessment of SVM, combining One-against-one and One-against-rest approaches with various kernels (Sigmoid, Polynomial, and RBF) to obtain optimal results. The highest accuracy rate of 96% is achieved using the One Against One method combined with the RBF kernel. This is closely followed by implementations using the Polynomial kernel at 95.2% and the Sigmoid kernel at 93.7%. When employing the One Against Rest method, the RBF kernel demonstrates superior performance with 95.5% accuracy compared to both Polynomial and Sigmoid kernels. Based on these evaluation results, it is evident that integrating the One Against One approach with the RBF kernel delivers the most optimal accuracy among all tested combinations. The sentiment class distribution in the optimal model classifies 49 tweets as positive, 927 as neutral, and 24 as negative.