Sentiment Analysis on the Centralized Isolation Policy for Covid-19 Response in Bali Province

Abstract

Coronavirus disease (Covid-19) is an infectious virus caused by SARS-CoV-2 virus, which has caused a global pandemic. The global pandemic is declared by the World Health Organization (WHO) on March 11th, 2020. Until late of April 2022, Covid-19 has caused the death of the total of more than 6 million people, and in Indonesia alone it has caused more than 150 thousand of death. The pandemic does not only impact health area, but also economy, politics, cultures, etc. In order to end the pandemic, WHO has sets out standard policies to be implemented, including by Indonesian government. Besides, each local goverment in Indonesia has also their own policies and regulations, including the government of Bali Province, for example the Community Activities Restrictions Enforcement or CARE (Indonesian: Pemberlakuan Pembatasan Kegiatan Masyarakat, commonly referred to as the PPKM), improvement in health care facilities and services, and also isolation policy for the people whom are infected by SARS-CoV-2 virus. To prevent the increase in death, Bali Province's COVID-19 Handling Task Force along with the district and city governments have prepared a centralized isolation policy and facilities for those whom are found to be Covid-19 positive without having any symptoms and for those with mild symtomps. This research has been conducted in order to get the public sentiment about this centralized isolation policy in Bali Province by using Tweet data from Twitter social media, based on Naive Bayes classification. From the experiments, the results of data Tweet classifications have accuracy more than 90%, which have shown that the public opinion is neutral. The results also mean that there is no contradition found in the application of centralized isolation policy in the province of Bali-Indonesia.

Keywords

Sentiment Analysis; Centralized Isolation; Covid-19; Bali Province

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References

F. K. K. R. Indonesia, “No Title,” 2022. https://www.kemkes.go.id/folder/view/full-content/structure-faq.html .

W. H. Organization, “Coronavirus disease (COVID-19),” 2022. https://www.who.int/health-topics/coronavirus#tab=tab_1.

A. Syauqi, “Jalan panjang covid19 (sebuah refleksi dikala wabah merajalela berdampak pada perekonomian),” JKUBS J. Chem. Inf. Model., vol. 1, no. 1, pp. 1–19, 2020.

R. Feldman and J. Sanger, The Text Mining Handbook. 2006.

Y. A. Mejova, Sentiment analysis within and across social media streams. 2012.

R. Feldman, “Techniques and Applications For Sentiment Analysis,” Commun. ACM, vol. 56, no. 4, pp. 82–89, 2013, doi: 10.1145/2436256.2436274.

Y. Garg and N. Chatterjee, “Sentiment analysis of twitter feeds,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8883, pp. 33–52, 2014, doi: 10.1007/978-3-319-13820-6.

A. Balahur et al., “Sentiment analysis in the news,” Proc. 7th Int. Conf. Lang. Resour. Eval. Lr. 2010, pp. 2216–2220, 2010.

S. Kiritchenko, X. Zhu, and S. M. Mohammad, “Sentiment analysis of short informal texts,” J. Artif. Intell. Res., vol. 50, pp. 723–762, 2014, doi: 10.1613/jair.4272.

A. Ortigosa, J. M. Martín, and R. M. Carro, “Sentiment analysis in Facebook and its application to e-learning,” Comput. Human Behav., vol. 31, pp. 527–541, Feb. 2014, doi: 10.1016/j.chb.2013.05.024.

W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, 2014, doi: 10.1016/j.asej.2014.04.011.

D. Berrar, “Bayes’ theorem and naive bayes classifier,” Encycl. Bioinforma. Comput. Biol. ABC Bioinforma., vol. 1–3, no. September, pp. 403–412, 2018, doi: 10.1016/B978-0-12-809633-8.20473-1.

A. Herdhianto, Sentiment Analysis Menggunakan Naïve Bayes Classifier (NBC) Pada Tweet Tentang Zakat. 2020.

S. D. Harijiatno, “ANALISIS SENTIMEN PADA TWITTER MENGGUNAKAN MULTINOMINAL NAIVE BAYES,” 2019.

I. Susianti, S. S. Ningsih, M. Al Haris, and T. W. Utami, “Analisis Sentimen Pada Twitter Terkait New Normal Dengan Metode Naïve Bayes Classifier,” Pros. Semin. Edusainstech FMIPA UNIMUS, pp. 354–363, 2020, [Online]. Available: https://prosiding.unimus.ac.id/index.php/edusaintek/article/view/576/578.

V. Jayaswal, “Text Vectorization: Term Frequency — Inverse Document Frequency (TFIDF),” 2020. https://towardsdatascience.com/text-vectorization-term-frequency-inverse-document-frequency-tfidf-5a3f9604da6d.

http://dx.doi.org/10.28989/compiler.v11i2.1356

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