IndoPolicyStats: sentiment analyzer for public policy issues

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


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.


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

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).