IndoPolicyStats: sentiment analyzer for public policy issues

Muhammad Noor Fakhruzzaman, Sa’Idah Zahrotul Jannah, Sie Wildan Gunawan, Angga Iryanto Pratama, Denise Arne Ardanty

Research output: Contribution to journalArticlepeer-review

Abstract

The government requires some vaccination for public health. This has led to a debate in recent years, especially during the Covid-19 pandemic. This research aims to analyze the two sentiments of the public regarding the vaccination policy. This would be helpful to ensure the acceptance of the government campaign about vaccination. The data used was text data obtained from Twitter when Indonesia was facing the second wave of the Covid-19 pandemic. The data were pre-processed by removing noise data, case folding, stemming, and tokenizing. Then, the data were classified with random forest, Naïve Bayes, and XGBoost. The results showed that all classifiers exhibit satisfying performance but XGBoost performs slightly better in accuracy value. This method can be deployed to be an automatic sentiment analyzer to help the government understand public feedback about its policies. This would be given by proper pre-processing and enough datasets.

Original languageEnglish
Pages (from-to)482-489
Number of pages8
JournalBulletin of Electrical Engineering and Informatics
Volume13
Issue number1
DOIs
Publication statusPublished - Feb 2024

Keywords

  • Covid-19
  • Pandemic
  • Public policy
  • Sentiment analysis
  • Vaccination

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