Sentiment Analysis About COVID-19 Booster Vaccine on Twitter Using Deep Learning

Authors

  • Elly Indrayuni Universitas Bina Sarana Informatika
  • Achmad Nurhadi Universitas Bina Sarana Informatika

DOI:

10.33395/sinkron.v7i3.11485

Abstract

The rapid spread of COVID-19 cases to various countries has made the COVID-19 outbreak a global pandemic by the World Health Organization (WHO). The effect of the designation of COVID-19 as a pandemic has prompted the government to take preventive action against vaccination, as well as the WHO which has asked the public to immediately get a third or booster dose of vaccine. Various responses regarding the COVID-19 booster vaccine continue to emerge on social media such as Twitter. Twitter is often used by its users to express emotions about something either positive or negative. People tend to believe what they find on social networks, which makes them vulnerable to rumors and fake news. Sentiment analysis or opinion mining is one solution to overcome the problem of automatically classifying opinions or reviews into positive or negative opinions. In this study, the Deep Learning algorithm was used to analyze public opinion sentiment regarding the COVID-19 booster vaccine on Twitter. The data collection method used is crawling data using an access token obtained from the Twitter API. Meanwhile, to evaluate the model, the K-fold Cross-Validation method is used. The results of testing the model obtained the highest accuracy value at iterations = 10, which is 82.78% with AUC value = 0.836, precision = 83.33% and recall = 95.89%.

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References

Alsaeedi, A., & Khan, M. Z. (2019). A study on sentiment analysis techniques of Twitter data. International Journal of Advanced Computer Science and Applications, 10(2), 361–374. https://doi.org/10.14569/ijacsa.2019.0100248

Arief, R., & Imanuel, K. (2019). Analisis Sentimen Topik Viral Desa Penari Pada Media Sosial Twitter Dengan Metode Lexicon Based. Jurnal Ilmiah Matrik, 21(3), 242–250. https://doi.org/10.33557/jurnalmatrik.v21i3.727

Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E. (2020). Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing Journal, 97, 106754. https://doi.org/10.1016/j.asoc.2020.106754

Eka Sembodo, J., Budi Setiawan, E., & Abdurahman Baizal, Z. (2016). Data Crawling Otomatis pada Twitter. October 2018, 11–16. https://doi.org/10.21108/indosc.2016.111

Haddi, E., Liu, X., & Shi, Y. (2013). The Role of Text Pre-processing in Sentiment Analysis. First International Conference on Information Technology and Quantitative Management, 17, 26–32. https://doi.org/10.1016/j.procs.2013.05.005

Himalay, P. (2021). What Happen in an Internet Minute - Bond High Plus. Bondhighplus.Com.

Indrayuni, E. (2018). Komparasi Algoritma Naive Bayes Dan Support Vector Machine Untuk Analisa Sentimen Review Film. Jurnal Pilar Nusa Mandiri, 14(2), 175. https://doi.org/10.33480/pilar.v14i2.918

Indrayuni, E., Nurhadi, A., & Kristiyanti, D. A. (2021). Implementasi Algoritma Naive Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk Analisa Sentimen Aplikasi Halodoc. Faktor Exacta, 14(2), 64. https://doi.org/10.30998/faktorexacta.v14i2.9697

Kaur, H., Ahsaan, S. U., Alankar, B., & Chang, V. (2021). A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets. Information Systems Frontiers, 23(6), 1417–1429. https://doi.org/10.1007/s10796-021-10135-7

Lestandy, M., Abdurrahim, A., & Syafa’ah, L. (2021). Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 802–808. https://doi.org/10.29207/resti.v5i4.3308

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., & Ghafoorian, M. Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.

Rachman, F. F., & Pramana, S. (2020). Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter. Health Information Management Journal, 8(2), 100–109. https://inohim.esaunggul.ac.id/index.php/INO/article/view/223/175

Ramdhani, N., & Al-Fadillah, R. H. (2021). Analisis Sentimen Pengguna Twitter Terhadap Belajar Daring Selama Pandemi Covid-19 Dengan Deep Learning. 7(2), 2021.

Samsir, Ambiyar, Verawardina, U., Edi, F., & Watrianthos, R. (2021). Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naive Bayes. Jurnal Media Informatika Budidarma, 5(1), 157. https://doi.org/10.30865/mib.v5i1.2604

Syarifuddin, M. (2020). Analisis Sentimen Opini Publik Terhadap Efek PSBB Pada Twitter Dengan Algoritma Decision Tree-KNN-Naive Bayes. Inti Nusa Mandiri, 15(1), 87–94. https://doi.org/10.33480/inti.v15i1.1433

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How to Cite

Elly Indrayuni, & Achmad Nurhadi. (2022). Sentiment Analysis About COVID-19 Booster Vaccine on Twitter Using Deep Learning. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(3), 900-905. https://doi.org/10.33395/sinkron.v7i3.11485