Sentiment Analysis for Opinions on the Covid-19 Vaccination Program Using a Naive Bayes Classifier

Authors

  • Hasna Melani Puspasari Centre of Data and System Information, The National Institute of Public Administration
  • Pujiatmo Subarkah Centre of Data and System Information, The National Institute of Public Administration

DOI:

https://doi.org/10.24258/jba.v18i3.992

Abstract

Implementing the COVID-19 vaccination program was not easy because it received various responses from the public. This study will explore public sentiment on the program which was taken based on public comments on several videos on YouTube using data crawling techniques using Coberry tools. The data was public opinions in Bahasa Indonesia that will be collected based on videos from official news channels with high engagement in the last eight months. Sentiment analysis was carried out using the Naive Bayes Classifier method that can be used to make deep analysis through filtering and classifying the opinions of the Indonesian people, as stated on YouTube. This research showed that public sentiment was dominated by negative sentiment based on some public doubts regarding the side effects of vaccines and the government follow-up regarding the country's economic recovery. Compared with the previous studies, the conclusions of sentiment obtained by this study were not the same due to differences in data sources and the selection of timeframes for community responses. An analysis of the public responses that have been carried out using a data collection method like this one will be very effective in providing an overview of the public desire to facilitate policymakers in formulating policy designs for the public interest

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Published

2022-11-30

How to Cite

Puspasari, H. M., & Subarkah, P. (2022). Sentiment Analysis for Opinions on the Covid-19 Vaccination Program Using a Naive Bayes Classifier. Jurnal Borneo Administrator, 18(3), 213–230. https://doi.org/10.24258/jba.v18i3.992

Issue

Vol. 18 No. 3 (2022): December 2022

Section

Articles

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Copyright (c) 2022 Author(s)

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