Sentiments Analysis of Indonesian Tweet About Covid-19 Vaccine Using Support Vector Machine and Fasttext Embedding

Vaccination is one of the efforts to overcome the COVID-19 pandemic in many countries, including Indonesia. Various responses through social media have also come from diverse levels of Indonesian society regarding the COVID-19 vaccine. Sentiment analysis about vaccines on social media is one way to investigates public responses to these efforts. This study proposes to develop a model that can analyze these sentiments by classifying the public responses on Twitter into positive, negative, and neutral sentiment classes. One of the success factors in sentiment analysis is the selection of the appropriate feature extraction. In general, tweets contain a lot of non-standard words. The fastText is a feature extraction that can handle non-standard word representations so that vectors can be presented as other standard words. Therefore, the proposed tweet sentiment analysis model consists of the fastText as a feature extractor and SVM as a classifier. This study utilizes 832 Indonesian tweets for experiment purposes. Furthermore, another feature extractor, WORD2VEC, and another classifier, MNB, are also used for comparison. The experimental results show that the fastText-SVM model has outperformed others in terms of accuracy, i.e., 88.10%.