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Sentiment Analysis on Covid-19 Vaccination in Indonesia Using Support Vector Machine and Random Forest

I Made Sumertajaya, Yenni Angraini, Jamaluddin Rabbani Harahap, Anwar Fitrianto

Abstract


World Health Organization (WHO) stated Covid-19 as a global pandemic in March, 2020. This pandemic has influenced people’s life in many sectors such as the economy, health, tourism, and many more. One way to end this pandemic is to make herd immunity obtained through the vaccination program. This program still raises pros and cons at the beginning of its implementation in Indonesia. Many people doubt the safety and side effects of the vaccine. There are also pros and cons to vaccination programs in social media such as Twitter. This platform generates a huge amount of text data containing people's perceptions about vaccines. This research aims to predict sentiment using supervised learning such as support vector machine (SVM) and random forest and capture sentiment about vaccines in Indonesia in the first two weeks of the program. The result shows SVM was a better model than random forest based on the precision and F1-score metrics. The SVM approach produces a precision value of 0.50, a recall of 0.64, and an F1-score of 0.52. In the study, it was also found that tweets with neutral sentiment dominated the twitter user sentiment in the study period. Tweets with negative sentiment decreased after the first week of the COVID-19 vaccination program.


Keywords


Coronavirus, Vaccination, Support Vector Machine, Random Forest, Twitter, Linear Discriminant AnalysisDA

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DOI: 10.30595/juita.v10i1.12394

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ISSN: 2579-8901