Sentiment Analysis Covid-19 Vaccination on Twitter Social Media Using Naïve Bayes Method Analisis Sentimen Masyarakat Terhadap Tindakan Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Metode Naïve Bayes
Article Sidebar
Main Article Content
Abstract
The government regulations regarding the implementation of vaccinations to tackle the COVID-19 pandemic. The regulation was issued by the Minister of Health Number ten of 2021. This program raises pros and cons so that it requires feedback for evaluation. Feedback can be obtained from opinions and stories that users convey through social media such as Twitter. This study aims to develop a model to determine public sentiment towards Covid-19 vaccination in three topics, namely the vaccination program, the effect of vaccination and the Covid-19 vaccine. The classification method used in this research is Bernoulli Naïve Bayes and Logistic Regression. The results of the comparison of the two methods show that Bernoulli Naïve Bayes gets better accuracy results. The number of tweet messages processed from Twitter is 5877. The model was tested to read public sentiment on Twitter from 7 September to 21 September 2021. The model concluded that public opinion regarding the vaccination program and the effect of vaccination tended to be positive. And opinions regarding the Covid-19 vaccine topic tend to be neutral. For further research, it can be developed by adding datasets.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[2] Kementerian Kesehatan RI, “Pmk No 10 Tentang Pelaksanaan Vaksinasi Dalam Rangka Penanggulangan Pandemi Corona Virus Disease 2019 (Covid-19),” 2021.
[3] Bing Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.
[4] A. Sasmito Aribowo, “Analisis Sentimen Publik pada Program Kesehatan Masyarakat menggunakan Twitter Opinion Mining,” in Seminar Nasional Informatika Medis (SNIMed), 2018, vol. 1, no. 1, pp. 17–23, [Online]. Available: https://journal.uii.ac.id/snimed/article/view/11877.
[5] M. Syarifuddin, “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.
[6] C.- Pandemic, B. Laurensz, and E. Sediyono, “Analisis Sentimen Masyarakat terhadap Tindakan Vaksinasi dalam Upaya Mengatasi Pandemi Covid-19,” vol. 10, no. 2, pp. 118–123, 2021.
[7] S. Mujilahwati, “Pre-Processing Text Mining Pada Data Twitter,” Semin. Nas. Teknol. Inf. dan Komun., vol. 2016, no. Sentika, pp. 2089–9815, 2016.
[8] M. Nurjannah and i. Fitri astuti, “Penerapan Algoritma Term Frequency-Inverse Document Frequency (Tf-Idf) Untuk Text Mining,” j. Inform. MULAWARMAN, VOL. 8, NO. 3, PP. 110–113, 2013.
[9] O. V. Putra, F. M. Wasmanson, T. Harmini, and S. N. Utama, “Sundanese Twitter Dataset for Emotion Classification,” CENIM 2020 - Proceeding Int. Conf. Comput. Eng. Network, Intell. Multimed. 2020, no. Cenim 2020, pp. 391–395, 2020, doi: 10.1109/CENIM51130.2020.9297929.
[10] S. Yadav and S. Shukla, “Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification,” in 2016 IEEE 6th International Conference on Advanced Computing (IACC), Feb. 2016, no. Cv, pp. 78–83, doi: 10.1109/IACC.2016.25.