Support Vector Machine for Sentiment Analysis of COVID-19 Vaccine

Abstract

This chapter discussed the use of social media, particularly Twitter, to analyze sentiments about the COVID-19 vaccine in Indonesia. With a high percentage of social media users in the country, sentiment analysis can be conducted to classify attitudes into three categories: positive, negative, and neutral. The chapter presents the Support Vector Machine (SVM) algorithm as one of the best ways to analyze sentiment and compares the effectiveness of the Radial Basis Function (RBF) and sigmoid kernel techniques in achieving accurate results. The results showed that both kernels had the same accuracy of 0.8075, but the RBF kernel had slightly better accuracy in the second iteration. This chapter suggested that sentiment analysis results can be used to educate people about the COVID-19 vaccine. It also emphasized the importance of social media and data analysis in understanding public sentiment, especially during a global health crisis.