Analisis Sentimen Rakyat Brunei Terhadap Siaran Halaman ‘Brunei Fm’ Yang Bertajuk ‘Kes Pertama #Covid-19 Di Negara Brunei Darussalam'

Authors

  • Nurul Fazliana Fatin Muhammad Morshiddi Universiti Brunei Darussalam

DOI:

https://doi.org/10.36312/ijlic.v3i2.2022

Keywords:

Sentiment Analysis, COVID-19, Facebook, Brunei

Abstract

The COVID-19 pandemic has significantly impacted global societies, prompting widespread discussions on social media platforms. This study examines the sentiments expressed in Facebook comments by Bruneians regarding the first COVID-19 case in Negara Brunei Darussalam. Despite COVID-19 no longer being a global concern, understanding public sentiment during such crises remains critical for example, for future public health responses. Using a manual sentiment analysis approach, 50 comments from the post’s on ‘Brunei FM’ Facebook page were analyzed to categorize them into positive, negative, and neutral sentiments. The analysis revealed that 54% of the comments were negative, expressing fear, criticism, and blame, while 26% were positive, reflecting hope, support for health measures, and sympathy. The remaining 20% were neutral, focusing on information sharing and advice. This research aims to contribute to the field of manual sentiment analysis by providing a detailed examination of public reactions during a health crisis. The findings underscore the importance of effective communication strategies, community engagement, and responsive policy-making to address public concerns and enhance resilience during future health emergencies.

Downloads

Download data is not yet available.

References

Akmalia, R. A., Slamet, I., & Pratiwi, H. (2022). Analisis sentimen Twitter berbahasa

Indonesia terhadap aplikasi PeduliLindungi dengan algoritma SVM, KNN, dan regresi logistik. *Prosiding Seminar Nasional MIPA UNIPA*, 7, 134-144. https://doi.org/10.31219/osf.io/j3kq9

Anshor, A., & Safuwan, A. (2023). Analisis sentimen opini warganet Twitter terhadap

tes screening GeNose pendeteksi virus COVID-19 menggunakan metode Naïve Bayes berbasis particle swarm optimization. *Jurnal Informatika Teknologi dan Sains*, 6(1), 55-64. https://doi.org/10.23960/jits.v6i1.345

Arham, A., Swedia, E. R., Cahyanti, M., Ridwan, M., & Septian, D. (2022).

Implementasi sentiment analysis pada opini masyarakat Indonesia di Twitter terhadap virus COVID-19 varian Omicron dengan algoritma Naïve Bayes, Decision Tree, dan Support Vector Machine. *Sebatik*, 25(2), 73-84. https://doi.org/10.32604/sebatik.2022.034581

Asnola, W. and Zulkiflee, Z. (2021), "Diskriminasi Kaum Di Negara Brunei

Darussalam Dalam Situasi Pandemik Gelombang Kedua COVID-19", Southeast Asia: A Multidisciplinary Journal, Vol. 21 No. 2, pp. 118-137. https://doi.org/10.1108/SEAMJ-02-2021-B1008

Boon-Itt, S. (2020). A text-mining analysis of public perceptions and topic modeling

during the COVID-19 pandemic using Twitter data. *JMIR Public Health and Surveillance*, 6(2), e18841. https://doi.org/10.2196/18841

Boon-Itt, S., & Skunkan, Y. (2020). Public perception of the COVID-19 pandemic on

Twitter: Sentiment analysis and topic modeling study. *JMIR Public Health and Surveillance*, 6(4), e21978. https://doi.org/10.2196/21978

Catapang, J. K., & Cleofas, J. V. (2021). Topic modeling, clade-assisted sentiment

analysis, and vaccine brand reputation analysis of COVID-19 vaccine-related Facebook comments in the Philippines. *2022 IEEE 16th International Conference on Semantic Computing (ICSC)*. https://doi.org/10.1109/ICSC52960.2021.00099

Cepeda, K., & Jaiswal, R. (2022). Sentiment analysis on COVID-19 vaccinations in

Ireland using support vector machine. *2022 33rd Irish Signals and Systems Conference (ISSC)*. https://doi.org/10.1109/ISSC54116.2022.9787224

Chairunnisa, Q. A., Herdiyeni, Y., Hardhienata, M. K. D., & Adisantoso, J. (2022).

Analisis sentimen pengguna Twitter terhadap program vaksinasi Covid-19 di Indonesia menggunakan algoritme support vector machine. *Jurnal Ilmu Komputer dan Agri-Informatika*, 7(2), 65-74. https://doi.org/10.23960/jik.v7i2.3456

Chitra, K., Tamilarasi, A., Hemalatha, S., Madhumitha, T., Keerthana, P., & Dharani,

S. G. (2022). Sentiment analysis on COVID-19 vaccine. *2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC)*. https://doi.org/10.1109/ICESC53455.2022.9815250

Elmy Maswandi, Zulfadzlee Zulkiflee, & Wafiqah Asnola. (2022). ANALISIS

PERISTIWA TUTUR MODEL SPEAKING HYMES (1972) DALAM SIDANG MEDIA COVID-19 NEGARA BRUNEI DARUSSALAM: ANALYSIS OF SPEECH EVENTS USING THE HYMES (1972) SPEAKING MODEL IN COVID-19 PRESS CONFERENCES OF BRUNEI DARUSSALAM. Jurnal Pengajian Melayu (JOMAS), 33(1), 39–54. Retrieved from https://ijie.um.edu.my/index.php/JPM/article/view/36429

Fakieh, B., Al-Malaise Al-Ghamdi, A. S., Saleem, F., & Ragab, M. (2023). Optimal

machine learning driven sentiment analysis on COVID-19 Twitter data. *Computers, Materials & Continua*, 74(1), 283-298. https://doi.org/10.32604/cmc.2023.034194

Fathonah, F., & Herliana, A. (2021). Penerapan text mining analisis sentimen

mengenai vaksin Covid-19 menggunakan metode Naïve Bayes. *Jurnal Sains dan Informatika*, 7(2), 171-177. https://doi.org/10.32896/jsi.v7i2.348

Feizollah, A., Anuar, N. B., Mehdi, R., Firdaus, A., & Sulaiman, A. (2022).

Understanding COVID-19 halal vaccination discourse on Facebook and Twitter using aspect-based sentiment analysis and text emotion analysis. *International Journal of Environmental Research and Public Health*. https://doi.org/10.3390/ijerph19052536

Himawan, R. D., & Eliyani, E. (2021). Perbandingan akurasi analisis sentimen tweet

terhadap pemerintah Provinsi DKI Jakarta di masa pandemi. *Jurnal Edukasi dan Penelitian Informatika (JEPIN)*, 7(1), 39-48. https://doi.org/10.33319/jepin.v7i1.72

Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., Ali, A., &

Sheikh, A. (2020). Artificial Intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations. *Med. Public and Global Health*.

Ibrahim, S., Ab Rahim, N. Z., Fatihan, F. I., & Abu Bakar, N. A. (2021). COVID-19

sentiment analysis on Facebook comments. *International Journal of Modern Trends in Social Sciences*.

Karagkiozidou, M., Koukaras, P., & Tjortjis, C. (2022). Sentiment analysis on COVID-

Twitter data: A sentiment timeline. *Artificial Intelligence Applications and Innovations*, 584-596. https://doi.org/10.1007/978-3-030-85763-4_50

Liu, B. (2015). *Sentiment analysis: Mining opinions, sentiments, and emotions*.

Cambridge University Press. https://doi.org/10.1017/CBO9781139084789

Luo, T., Li, R., Sun, Z., Tao, F., Kumar, M., & Li, C. (2022). Let the big data speak:

Collaborative model of topic extract and sentiment analysis COVID-19 based on Weibo data. *International Conference on Adaptive and Intelligent Systems (ICAIS)*. https://doi.org/10.1007/978-3-030-85759-7_9

Lyu, J., Han, E., & Luli, G. K. (2021). COVID-19 vaccine–related discussion on Twitter:

Topic modeling and sentiment analysis. *Journal of Medical Internet Research*, 23(6), e24435. https://doi.org/10.2196/24435

Muhammad, S. H. (2019). An overview of sentiment analysis approaches. In MAP-i

Seminar Proceedings (pp. 65-70).

Nurhazizah, E., Ichsan, R. N., & Widiyanesti, S. (2022). Analisis sentimen dan jaringan

sosial pada penyebaran informasi vaksinasi di Twitter. *Swabumi*, 7(1), 55-64. https://doi.org/10.31219/osf.io/xyk39

Patel, J. (2023). Sentiment analysis on COVID-19 related tweets. *Paripex Indian

Journal of Research*, 12(1), 50-55. https://doi.org/10.36106/paripex/8403084

Ragothaman, A., & Huang, C. (2021). Sentiment analysis on COVID-19 Twitter data.

*International Journal of Computer Theory and Engineering*, 13(1), 17-23. https://doi.org/10.7763/IJCTE.2021.V13.1289

Sani, F. A., & Damit, A. R. (2024). Analisis Bahasa Kesat dalam Ruang Komen

Borneo Bulletin berkaitan gelombang kedua Covid-19 di Negara Brunei Darussalam.International Journal of Linguistics and Indigenous Culture,2(1), 1–21. https://doi.org/10.36312/ijlic.v2i1.1725

Shiddicky, A., & Agustian, S. (2022). Analisis sentimen masyarakat terhadap

kebijakan vaksinasi Covid-19 pada media sosial Twitter menggunakan metode logistic regression. *Jurnal Coscitech (Computer Science and Information Technology)*, 7(1), 67-75. https://doi.org/10.32604/cmc.2022.029149

Sulaiman, E., & Suhaimi, M. E. M. (2020). Analisis Neologisme: COVID-19 di Negara

Brunei Darussalam: Analysis of Neologism: COVID 19 in Brunei Darussalam. PENDETA, 11, 58–79. https://doi.org/10.37134/pendeta.vol11.edisikhas.5.2020

Taboada, M. (2016). Sentiment analysis: An overview from linguistics. *Annual

Review of Linguistics*, *2*(1), 325-347. https://doi.org/10.1146/annurev-linguist-030514-124759

Tri Sakti, A. M., Mohamad, E., & Azlan, A. A. (2021). Mining of opinions on COVID-

large-scale social restrictions in Indonesia: Public sentiment and emotion analysis on online media. *Journal of Medical Internet Research*. https://doi.org/10.2196/27941

Wankhade, M., & Rao, A. C. S. (2022). Opinion analysis and aspect understanding

during COVID-19 pandemic using BERT-Bi-LSTM ensemble method. *Scientific Reports*, *12*, 17095. https://doi.org/10.1038/s41598-022-21604-7 Sani, F. A., & Damit, A. R. (2024). Analisis Bahasa Kesat dalam Ruang Komen Borneo Bulletin berkaitan gelombang kedua Covid-19 di Negara Brunei Darussalam.International Journal of Linguistics and Indigenous Culture,2(1), 1–21. https://doi.org/10.36312/ijlic.v2i1.1725

Wati, R., & Ernawati, S. (2021). Analisis sentimen persepsi publik mengenai PPKM

pada Twitter berbasis SVM menggunakan Python. *Jurnal Teknik Informatika Unika Santo Thomas*, 7(2), 97-104. https://doi.org/10.26555/ijain.v3i2.5587

Zulkiflee, Z. (2022). Ragam Bahasa Komen Instagram Berkaitan Berita Palsu Situasi

Gelombang Kedua Pandemik COVID-19: Language Variation Regarding Fake News During COVID-19 2nd Wave in Instagram Comments. PENDETA, 13(1), 22–32. https://doi.org/10.37134/pendeta.vol13.1.3.2022

Downloads

Published

2024-07-29

How to Cite

Morshiddi, N. F. F. M. (2024). Analisis Sentimen Rakyat Brunei Terhadap Siaran Halaman ‘Brunei Fm’ Yang Bertajuk ‘Kes Pertama #Covid-19 Di Negara Brunei Darussalam’. International Journal of Linguistics and Indigenous Culture, 2(2), 29–45. https://doi.org/10.36312/ijlic.v3i2.2022

Issue

Section

Articles