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Analysis of Government Policy Sentiment Regarding Vacation during the COVID-19 Pandemic Using the Bidirectional Encoder Representation from Transformers (BERT)

Author

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  • Intan Nurma Yulita

    (Research Center for Artificial Intelligence and Big Data, Universitas Padjadjaran, Bandung 40132, Indonesia)

  • Victor Wijaya

    (Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Rudi Rosadi

    (Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Indra Sarathan

    (Faculty of Cultural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Yusa Djuyandi

    (Faculty of Social and Political Science, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Anton Satria Prabuwono

    (Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Rabigh 21911, Saudi Arabia)

Abstract

To address the COVID-19 situation in Indonesia, the Indonesian government has adopted a number of policies. One of them is a vacation-related policy. Government measures with regard to this vacation policy have produced a wide range of viewpoints in society, which have been extensively shared on social media, including YouTube. However, there has not been any computerized system developed to date that can assess people’s social media reactions. Therefore, this paper provides a sentiment analysis application to this government policy by employing a bidirectional encoder representation from transformers (BERT) approach. The study method began with data collecting, data labeling, data preprocessing, BERT model training, and model evaluation. This study created a new dataset for this topic. The data were collected from the comments section of YouTube, and were categorized into three categories: positive, neutral, and negative. This research yielded an F-score of 84.33%. Another contribution from this study regards the methodology for processing sentiment analysis in Indonesian. In addition, the model was created as an application using the Python programming language and the Flask framework. The government can learn the extent to which the public accepts the policies that have been implemented by utilizing this research.

Suggested Citation

  • Intan Nurma Yulita & Victor Wijaya & Rudi Rosadi & Indra Sarathan & Yusa Djuyandi & Anton Satria Prabuwono, 2023. "Analysis of Government Policy Sentiment Regarding Vacation during the COVID-19 Pandemic Using the Bidirectional Encoder Representation from Transformers (BERT)," Data, MDPI, vol. 8(3), pages 1-17, February.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:3:p:46-:d:1078053
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    References listed on IDEAS

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    5. Zaher Salah & Abdel-Rahman F. Al-Ghuwairi & Aladdin Baarah & Ahmad Aloqaily & Bar'a Qadoumi & Momen Alhayek & Bushra Alhijawi, 2019. "A systematic review on opinion mining and sentiment analysis in social media," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 31(4), pages 530-554.
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