Main Article Content

Abstract

On March 11, 2020, the World Health Organization (WHO) officially declared COVID-19 a global pandemic. The spread of this virus already existed in 2019 in the city of Wuhan, China. The government officially stipulates a Presidential Regulation (PERPRES) on Vaccine Procurement and Vaccination Implementation in the Context of Overcoming the Coronavirus Disease Pandemic. The vaccination activity plan must also consider various inputs, among them is by looking at how the response and public opinion to the vaccination discourse. By utilizing data from Twitter social media, this study aims to analyze the public's response to the vaccination discourse by classifying the response into positive and negative responses. Furthermore, public opinion grouping will also be carried out using the pre-trained Indonesian RoBERTa Base Sentiment Classifier model to find out the sentiments of the Covid-19 vaccination topic discussed by the community. The results of the analysis showed that the public gave more negative responses to the discourse (24.7%) compared to positive responses (5.7%) with the remaining neutral responses (69.6%). Sentiment words that occur most often also indicate more words with negative sentiments than words with positive sentiments. The average results of the prediction accuracy of the application of the pre-trainer model on the positive label are 84%, Neutral 97% and Negative 93%.

Article Details

How to Cite
zain, M. mahrus. (2021). Analysis of Public Opinion Sentiment on Covid-19 Vaccine on Twitter Social Media with Robustly Optimized BERT Pretraining Approach. Jurnal Komputer Terapan , 7(2), 280–289. https://doi.org/10.35143/jkt.v7i2.4782

References

  1. S. S. Al Jameel et al., “A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia,” Int. J. Environ. Res. Public Health, vol. 18, no. 1, p. 218, 2020, doi: 10.3390/ijerph18010218
  2. Liu C, Zhou Q, Li Y, Garner L V, Watkins SP, Carter LJ, et al. Research and Development on Therapeutic Agents and Vaccines for COVID-19 and Related Human Coronavirus Diseases. 2020
  3. F. F. Rachman and S. Pramana, “Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter,” Indones. Heal. Inf. Manag. J., vol. 8, no. 2, pp. 100– 109, 2020
  4. Wongso, W. (n.d.). w11wo/indonesian-roberta-base-sentiment-classifier Hugging Face. Retrieved from huggingface.co: https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier
  5. A. Harun and D. P. Ananda, “Analisa Sentimen Opini Publik Tentang Vaksinasi Covid-19 di Indonesia Menggunakan Naïve Bayes dan Decision Tree”. 2021. Retrieved from https://journal.irpi.or.id/index.php/malcom/article/view/63/31
  6. S. Christina, “Sarcasm in Sentiment Analysis of Indonesian Text: A Literature Review”. 2019. Retrieved from https://media.neliti.com/media/publications/294966-sarcasm-in-sentiment-analysis-of-indones-bd55d2e2.pdf
  7. Peraturan Presiden (PERPRES) Nomor 99 Tahun 2020
  8. K. Rajeswari and P. Shanthibala, "Recognization of Sarcastic Emotions of Individuals on Social Network," Int. J. Pure Appl. Math., vol. 118, no. 7, pp. 253-259, 2018
  9. E. Lunando and A. Purwarianti, “Indonesian Social Media Sentiment Analysis with Sarcasm Detection,” J. Sarj, Inst. Teknol. Bandung Bid. Tek. Elektro dan Inform., 2013.
  10. D. Hernikawati, “Kecenderungan Tanggapan Masyarakat Terhadap Vaksin Sinovac Berdasarkan Lexicon Based Sentiment Analysis,” J. IPTEK-KOM., 2021
  11. Bing Liu.(2010). Opinion Mining. Departemen of Computer Science, University of Illinois at Chicago
  12. Ronen Feldman. (2008). Applied Text Mining. Information Systems Department School Of Business Administration Hebrew University, Jerusalem
  13. RoBERTa: Pendekatan Pra-pelatihan BERT yang Dioptimalkan dengan Kua
  14. https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier
  15. Matulatuwa, Febrilien Matresya, Eko Sediyono, and Ade Iriani. 2017. “Text Mining Dengan Metode Lexicon Based Untuk Sentiment Analysis Pelayanan PT. Pos Indonesia Melalui Media Sosial Twitter.” Jurnal Masyarakat Informatika Indonesia 2 (3): 52–65