APPLICATION OF LEXICON BASED FOR SENTIMENT ANALYSIS OF COVID-19 BOOSTER VACCINATIONS ON TWITTER SOCIAL MEDIA USING NAÏVE BAYES METHOD

Muhamad Fahmi; Syarif Hidayat; Ahmad Fathan  Hidayatullah

doi:10.20884/1.jutif.2022.3.4.565

Muhamad Fahmi Informatics Master Study Program, Industrial Technology Faculty, Universitas Islam Indonesia, Indonesia
Syarif Hidayat Informatics Master Study Program, Industrial Technology Faculty, Universitas Islam Indonesia, Indonesia
Ahmad Fathan Hidayatullah Informatics Master Study Program, Industrial Technology Faculty, Universitas Islam Indonesia, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.4.565

Keywords: covid-19, lexicon based, logistic regression method, naïve bayes method, sentiment analysis, twitter, vaccination booster

Abstract

To combat the Covid-19 epidemic, the government issues laws governing vaccination implementation. Health Minister Number Ten of 2021 issued the regulation. This program raises advantages and disadvantages, necessitating examination through feedback. The opinions and narratives that individuals share on social media sites like Twitter can be used to get feedback. This work seeks to construct a model to assess public opinion of the Covid-19 Booster Vaccination by using the Lexicon Based technique to identify sentiment on tweet data. Naïve Bayes and logistic regression are the classification techniques employed in this study. The comparison of the two methods' findings reveals that Logistic Regression, with an accuracy of 72%, is superior to Naïve Bayes, which has an accuracy of 70%. There were 607 tweet messages from Twitter that were processed. From January 1 to July 30, 2022, the model was tested for its ability to interpret public opinion on Twitter. The model found that people's attitudes toward the COVID-19 booster shot tended to be favorable. It can be developed by including datasets for additional research. For further research, it can be developed by adding datasets.

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References

A. Prabhat and V. Khullar, “Sentiment classification on big data using Naïve bayes and logistic regression,” 2017 Int. Conf. Comput. Commun. Informatics, ICCCI 2017, no. March, 2017, doi: 10.1109/ICCCI.2017.8117734.

Y. Nooryuda Prasetya and D. Winarso, “Penerapan Lexicon Based Untuk Analisis Sentimen Pada Twiter Terhadap Isu Covid-19,” J. Fasilkom, vol. 11, no. 2, pp. 97–103, 2021.

M. Syarifuddinn, “Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn,” INTI Nusa Mandiri, vol. 15, no. 1, pp. 23–28, 2020, doi: 10.33480/inti.v15i1.1347.

B. Laurensz and Eko Sediyono, “Analisis Sentimen Masyarakat terhadap Tindakan Vaksinasi dalam Upaya Mengatasi Pandemi Covid-19,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 10, no. 2, pp. 118–123, 2021, doi: 10.22146/jnteti.v10i2.1421.

K. Jahanbin, V. Rahmanian, N. Sharifi, and F. Rahmanian, “Sentiment Analysis and Opinion Mining about COVID-19 vaccines of Twitter Data,” Pakistan J. Med. & Heal. Sci., vol. 15, no. 1, pp. 694–695, 2021.

A. Syakur, “Implementasi Metode Lexicon Base Untuk Analisis Sentimen Kebijakan Pemerintah Dalam Pencegahan Penyebaran Virus Corona Covid-19 Pada Twitter,” J. Ilm. Inform. Komput., vol. 26, no. 3, pp. 247–260, 2021, doi: 10.35760/ik.2021.v26i3.4720.

G. N. Aulia and E. Patriya, “Implementasi Lexicon Based Dan Naive Bayes Pada Analisis Sentimen Pengguna Twitter Topik Pemilihan Presiden 2019,” J. Ilm. Inform. Komput., vol. 24, no. 2, pp. 140–153, 2019, doi: 10.35760/ik.2019.v24i2.2369.

D. Muriyatmoko, T. Harmini, and M. K. Ardiansyah, “Sentiment Analysis Covid-19 Vaccination on Twitter Social Media Using Naïve Bayes Method,” Procedia Eng. Life Sci., vol. 2, no. 2, 2021, doi: 10.21070/pels.v2i0.1144.

A. Sasmito Aribowo, “Analisis Sentimen Publik pada Program Kesehatan Masyarakat menggunakan Twitter Opinion Mining,” Semin. Nas. Inform. Medis, vol. 0, no. 0, pp. 17–23, 2018, [Online]. Available: https://journal.uii.ac.id/snimed/article/view/11877.

A. Muzaki and A. Witanti, “Sentiment Analysis of the Community in the Twitter To the 2020 Election in Pandemic Covid-19 By Method Naive Bayes Classifier,” J. Tek. Inform., vol. 2, no. 2, pp. 101–107, 2021, doi: 10.20884/1.jutif.2021.2.2.51.

S. Sudianto, P. Wahyuningtias, H. W. Utami, U. A. Raihan, and H. N. Hanifah, “Comparison Of Random Forest And Support Vector Machine Methods On Twitter Sentiment Analysis ( Case Study : Internet Selebgram Rachel Vennya Escape From Quarantine ) Perbandingan Metode Random Forest Dan Support Vector Machine Pada Analisis Sentimen Twitt,” Jutif, vol. 3, no. 1, pp. 141–145, 2022.

R. D. Septiana, A. B. Susanto, and T. Tukiyat, “Analisis Sentimen Vaksinasi Covid-19 Pada Twitter Menggunakan Naive Bayes Classifier Dengan Feature Selection Chi-Squared Statistic dan Particle Swarm Optimization,” J. SISKOM-KB (Sistem Komput. dan Kecerdasan Buatan), vol. 5, no. 1, pp. 49–56, 2021, doi: 10.47970/siskom-kb.v5i1.228.

M. Kartika, S. Saepudin, and D. Gustian, “Analisis Sentimen Dampak Covid-19 Terhadap Pembatalan Keberangkatan Ibadah Haji Pada Tahun 2020,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 964–972, 2021.

A. F. Hidayatullah, S. Cahyaningtyas, and A. M. Hakim, “Sentiment Analysis on Twitter using Neural Network: Indonesian Presidential Election 2019 Dataset,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1077, no. 1, p. 012001, 2021, doi: 10.1088/1757-899x/1077/1/012001.

K. Hikmah, A. C. Fauzan, and Harliana, “Sentiment Analysis of Vaccine Booster during Covid-19- Indonesian Netizen Perspective Based on Twitter Dataset,” no. 22, pp. 102–106, 2022, [Online]. Available: http://ojs.stmikpringsewu.ac.id/index.php/jtksi.

N. A. Alkhaldi, Y. Asiri, A. M. Mashraqi, H. T. Halawani, S. Abdel-Khalek, and R. F. Mansour, “Leveraging Tweets for Artificial Intelligence Driven Sentiment Analysis on the COVID-19 Pandemic,” Healthcare, vol. 10, no. 5, p. 910, 2022, doi: 10.3390/healthcare10050910.