Sentiment Analysis to analyze Vaccine Enthusiasm in Indonesia on Twitter Social Media

  • M. Khairul Anam STMIK Amik Riau
  • Rahmaddeni
  • Muhammad Bambang Firdaus Mulawarman University
  • Hadi Asnal
  • Hamdani
Keywords: Vaccines, Tweet, Sentiment Analysis, Naïve bayes

Abstract

Vaccines are one way to prevent the coronavirus from entering the human body, although it is not 100% accurate. However, the implementation of vaccination in Indonesia is still controversial. People give their opinions directly or through social media such as Twitter. Retrieval of tweets using the Twitter API and using python. The data obtained is then preprocessed using case folding, cleaning, tokenizing, filtering, and stemming. After that, the model was evaluated using the Naive Bayes method. Naïve Bayes is a classification method that can predict the probability of a class to produce decisions based on learning data. Currently, nave Bayes is one of the methods to find accuracy in sentiment analysis that is often used and is the best. The results of this study obtained an accuracy of 79%.

References

G. Appel, L. Grewal, R. Hadi, and A. T. Stephen, “The future of social media in marketing,” J. Acad. Mark. Sci., vol. 48, pp. 79–95, 20219, doi: 10.1007/s11747-019-00695-1.

V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment analysis of social media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm,” in Procedia Computer Science, 2019, vol. 161, pp. 765–772, doi: 10.1016/j.procs.2019.11.181.

I. Febrianti, M. K. Anam, Rahmiati, and Tashid, “Tren Milenial Memilih Jurusan Di Perguruan Tinggi Menggunakan Metode Social Network Analysis,” Techo.COM, vol. 19, no. 3, pp. 216–226, 2020, doi: https://doi.org/10.33633/tc.v19i3.3483.

M. K. Anam, T. P. Lestari, Latifah, M. B. Firdaus, and S. Fadli, “Analisis Kesiapan Masyarakat Pada Penerapan Smart City di Sosial Media Menggunakan SNA,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 69–81, 2021, doi: https://doi.org/10.29207/resti.v5i1.2742.

J. Rodríguez-Ruiz, J. I. Mata-Sánchez, R. Monroy, O. Loyola-González, and A. López-Cuevas, “A one-class classification approach for bot detection on Twitter,” Comput. Secur., vol. 91, pp. 1–14, 2020, doi: 10.1016/j.cose.2020.101715.

F. E. Ayo, O. Folorunso, F. T. Ibharalu, I. A. Osinuga, and A. Abayomi-Alli, “A probabilistic clustering model for hate speech classification in twitter,” Expert Syst. Appl., vol. 173, no. February, p. 114762, 2021, doi: 10.1016/j.eswa.2021.114762.

H. U. Khan, S. Nasir, K. Nasim, D. Shabbir, and A. Mahmood, “Twitter trends: A ranking algorithm analysis on real time data,” Expert Syst. Appl., vol. 164, no. September 2020, p. 113990, 2021, doi: 10.1016/j.eswa.2020.113990.

T. El-Elimat, M. M. AbuAlSamen, B. A. Almomani, N. A. Al-Sawalha, and F. Q. Alali, “Acceptance and attitudes toward COVID-19 vaccines: A cross-sectional study from Jordan,” PLoS ONE, vol. 16, no. 4 April. 2021, doi: 10.1371/journal.pone.0250555.

M. K. Anam, “Analisis Respons Netizen Terhadap Berita Politik Di Media Online,” J. Ilm. Ilmu Komput., vol. 3, no. 1, pp. 14–21, 2017, doi: 10.35329/jiik.v3i1.62.

R. N. Rahayu and Sensusiyati, “Vaksin covid 19 di indonesia : analisis berita hoax,” Intelektiva J. Ekon. Sos. Hum. Vaksin, vol. 2, no. 07, pp. 39–49, 2021.

P. J. Turner et al., “COVID-19 vaccine-associated anaphylaxis: A statement of the World Allergy Organization Anaphylaxis Committee,” World Allergy Organ. J., vol. 14, no. 2, p. 100517, 2021, doi: 10.1016/j.waojou.2021.100517.

T. Le, “An attention-based deep learning method for sentiment analysis,” in Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, 2020, pp. 282–286, doi: 10.1109/CSCI51800.2020.00054.

M. Demircan, A. Seller, F. Abut, and M. F. Akay, “Developing Turkish sentiment analysis models using machine learning and e-commerce data,” International Journal of Cognitive Computing in Engineering, vol. 2. pp. 202–207, 2021, doi: 10.1016/j.ijcce.2021.11.003.

R. N. Devita, H. W. Herwanto, and A. P. Wibawa, “Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, p. 427, 2018, doi: 10.25126/jtiik.201854773.

W. Baswardono, D. Kurniadi, A. Mulyani, and D. M. Arifin, “Comparative analysis of decision tree algorithms: Random forest and C4.5 for airlines customer satisfaction classification,” in Journal of Physics: Conference Series, 2019, vol. 1402, no. 6, doi: 10.1088/1742-6596/1402/6/066055.

F. V. Sari and A. Wibowo, “Analisis Sentimen Pelanggan Toko Online Jd. Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 2, no. 2, pp. 681–686, 2019, doi: https://doi.org/10.24176/simet.v10i2.3487.

B. Gunawan, H. S. Pratiwi, and E. E. Pratama, “Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 4, no. 2, p. 113, 2018, doi: 10.26418/jp.v4i2.27526.

V. A. Permadi, “Analisis Sentimen Menggunakan Algoritma Naive Bayes Terhadap Review Restoran di Singapura,” J. Buana Inform., vol. 11, no. 2, p. 140, 2020, doi: 10.24002/jbi.v11i2.3769.

G. P. A. Brahmantha and I. W. Santiyasa, “Sentiment Analysis of the Enforcement of PSBB Part II in Jakarta,” JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 9, no. 2, p. 259, 2020, doi: 10.24843/jlk.2020.v09.i02.p13.

E. Haddi, X. Liu, and Y. Shi, “The role of text pre-processing in sentiment analysis,” Procedia Comput. Sci., vol. 17, pp. 26–32, 2013, doi: 10.1016/j.procs.2013.05.005.

H. M. Zin, N. Mustapha, M. A. A. Murad, and N. M. Sharef, “The effects of pre-processing strategies in sentiment analysis of online movie reviews,” in AIP Conference Proceedings, 2017, vol. 1891, doi: 10.1063/1.5005422.

W. Gata and Purnomo, “Akurasi Text Mining Menggunakan Algoritma K-Nearest Neighbour pada Data Content Berita SMS,” J. Format, vol. 6, no. 1, pp. 1–13, 2017.

N. Z. Dina and N. Juniarta, “Aspect based Sentiment Analysis of Employee’s Review Experience,” J. Inf. Syst. Eng. Bus. Intell., vol. 6, no. 1, p. 79, 2020, doi: 10.20473/jisebi.6.1.79-88.

M. N. Saadah, R. W. Atmagi, D. S. Rahayu, and A. Z. Arifin, “Information Retrieval Of Text Document With Weighting Tf-Idf And Lcs,” J. Comput. Sci. Inf., vol. 6, no. 1, pp. 34–37, 2013, doi: https://doi.org/10.21609/jiki.v6i1.216.

M. N. Saadah, R. W. Atmagi, D. S. Rahayu, and A. Z. Arifin, “Sistem Temu Kembali Dokumen Teks Dengan Pembobotan Tf-Idf Dan Lcs,” JUTI J. Ilm. Teknol. Inf., vol. 11, no. 1, p. 19, 2013, doi: 10.12962/j24068535.v11i1.a16.

Published
2021-12-30