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

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.

Sentiment analysis of Indonesian tweets on COVID-19 and COVID-19 vaccinations

“…Sumertajaya et al chose a time frame of January 15, 2021 to January 28, 2021, for the reason that this was the first period of the COVID-19 vaccination program being launched in Indonesia. 31 While Agustiningsih et al captured tweets within September 2021. 32 Both used different method of learning framework.…”

“…Sumertajaya et al implemented support vector machine (SVM) and random forest, while Agustiningsih et al employed bidirectional Long Short-Term Memory (LSTM) combined with word embedding. 31 , 32 …”

Sentiment classification from reviews for tourism analytics

“…It is an excellent tool for businesses to analyse opinions expressed by users on social media without explicitly asking any questions, as this approach often reflects their genuine thoughts [18] [19]. The increasing number of daily active users on social media makes it a potential data source for sentiment analysis as it could observe people's behaviors and opinions in textual form [11]. Sentiment analysis is widely used in many domains such as education, healthcare, politics, e-commerce and many more [20] [21] [22] [23].…”

“…In paper [10] , airline passenger reviews were classified into 5 categories: plane condition, flight comfort, staff service, food and entertainment and price using the Bayes and Support Vector Machine method to understand the satisfaction levels of the passenger for these categories. Moreover, people's perceptions regarding vaccines in Indonesia on Twitter were captured in the first two weeks to predict the people's sentiment about it by using Support Vector Machine and Random Forest [11]. Majority of the previous research on sentiment analysis uses Naïve Bayes, Support Vector Machine and Random Forest as their Machine Learning classifiers.…”