A Sentiment Analysis Model for the COVID-19 Vaccine in Indonesia Using Twitter API v2, TextBlob, and Googletrans

Abstract

COVID-19 was declared a world pandemic in early-mid 2020. After about a year, several vaccines for this virus have been found and become alternative solutions to reduce the spread of the COVID-19 pandemic and build up herd immunity in society. Since early 2021, Indonesia has been one of the countries that participate in using vaccines for the public to fight the COVID-19 pandemic. However, there are a lot of positive and negative responses from Indonesian society related to these COVID-19 vaccines. Implementing a sentiment analysis model for a specific topic like a “vaccine” from social media could help us to see and understand the responses from society in Indonesia towards the vaccine program that is being conducted by the Indonesian government. Understanding society's response towards vaccines is expected to be able to support the Indonesian government, for example in formalizing the distribution strategy of vaccines in the future. This paper discusses how to develop a sentiment analysis model, by implementing several existing technologies such as Twitter API, TextBlob, and Googletrans Python libraries. The utilization of these existing technologies could show how a sentiment analysis model could be developed conveniently, for example in using cases to analyse Indonesian society's responses towards the COVID-19 vaccine program. Besides the solution design, this paper also shows a sample of data visualization of the sentiment analysis model in a meaningful infographics format.