Support Vector Machine Method with Word2vec for Covid-19 Vaccine Sentiment Classification on Twitter

Abstract

Covid-19 has been a dangerous outbreak for the world that has lasted more than 2 years. Covid-19 has evolved or developed into several new variations, such as delta which is more dangerous than its initial variant. Vaccines became the world's solution to defend against Covid-19. In Indonesia, at the early stages of implementing mass vaccination programs, people had been involved in many pros and cons, to support or against the program. On social media such as Twitter, public opinions about vaccines are very diverse. This study investigates public sentiment towards the early stage of vaccination program conducted by the government. The classification method used in the sentiment analysis is the Support Vector Machine (SVM), among the positive, negative and neutral classes, with word embeddings extraction features. Data was collected and labeled by 12 crowd sourced annotators. The training and parameter tuning process was carried out to find the model that produced the best accuracy of validation data. From 400 testing data, the application of this optimal model resulted in an F1-score of 65% and an accuracy of 69%, higher than several machine learning methods in the same study.