Sentiment Analysis Concerning Indonesian Community Acceptance of COVID-19 Vaccine in Social Media

Aisyah Larasati; Deni Prastyo; Agus Rachmad Purnama

doi:10.1201/9781003331674-22

Sentiment Analysis Concerning Indonesian Community Acceptance of COVID-19 Vaccine in Social Media

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Aisyah Larasati, Deni Prastyo, Agus Rachmad Purnama

2024 The Spirit of Recovery: IT Perspectives, Experiences, and Applications during the COVID-19 Pandemic Book chapter Cited by 0

Abstract

Information flows fast, and social media has much of it. Twitter has a vast database, but most of it is unclear. Text mining helps uncover information in unstructured text. Twitter users searched for COVID-19 vaccine information most during the outbreak. The COVID-19 vaccination dispute continues on social media, with pros and drawbacks. Hence, data analysis is needed to understand COVID-19 vaccination public opinion better. The sentiment model analyzes text data to identify sentiments about the government’s program. Comparing classification performance requires the optimization algorithm. Neural Network (NN) and Support Vector Machine (SVM) sentiment studies determine COVID-19 vaccination program public opinion. SVM performs best with an 88% Receiver Operating Characteristic (ROC) score. The study emphasizes the need to enhance algorithms for text mining and sentiment analysis to determine and comprehend public opinion on an issue. © 2024 selection and editorial matter, Aji Prasetya Wibawa.

Affiliations

Universitas Negeri Malang, Malang, Indonesia; Universitas Nahdlatul Ulama Sidoarjo, Sidoarjo, Indonesia

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