Predicting Handling Covid-19 Opinion using Naive Bayes and TF-IDF for Polarity Detection
DOI:
https://doi.org/10.30812/matrik.v22i2.2227Keywords:
Covid-19, Naïve bayes, Predicting, Public opinion, TF-IDF, TwitterAbstract
There are many public responses about implementing government policies related to Covid-19. Some have positive and negative opinions, especially on the official social media portal of the government. Twitter is one social media where people are free to express their opinions. This study aims to find out the opinion of sentiment analysis on Twitter in implementing government policies related to Covid-19 to classify public opinion. Several stages in analyzing public sentiment are taken from the tweet data. The first step is data mining to get the tweets that will be analyzed later. Furthermore, cleaning tweet data and equalizing tweet data into lowercase. After that, perform the tweet's basic word search process and calculate its appearance frequency. Then calculate using the Naïve Bayes method and determine the sentiment classification of the tweet. The results showed that Indonesia's public sentiment about covid-19 prevention is neutral. The performance of the application shows an Accuracy value of 76.7%. In conclusion this means that the Indonesian government needs to evaluate the policies taken to deal with COVID-19 to create positive opinions to create solid cooperation between the government and the government. Residents in tackling the COVID-19 outbreak.
Downloads
Download data is not yet available.
References
[1] A. L. Fairuz, R. D. Ramadhani, and N. A. F. Tanjung, “Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter,†JURNAL DINDA, vol. 1, no. 1, pp. 42–51, 2021.
[2] S. Supangat and M. Bin Saringat, “Development of E-Learning System Using Felder and Silverman’s Index of Learning Styles Model,†International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 5, pp. 8554–8561, 2020, doi: 10.30534/ijatcse/2020/236952020.
[3] M. Ardan, F. F. Rahman, and G. B. Geroda, “The influence of physical distance to student anxiety on COVID-19, Indonesia,†Journal of Critical Reviews, vol. 7, no. 17, pp. 1126–1132, 2020, doi: 10.31838/jcr.07.17.141.
[4] A. Faesal, A. Muslim, A. H. Ruger, and K. Kusrini, “Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 2, pp. 207–213, May 2020, doi: 10.30812/matrik.v19i2.640.
[5] H. H. Hidayat, A. Ardiansyah, P. Arsil, and L. I. Rahmawati, “Pemetaan Kata Kunci dan Polaritas Sentimen Pengguna Twitter Terhadap Kehalalan Produk,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 1–10, Nov. 2021, doi: 10.30812/matrik.v21i1.1162.
[6] H. AlSalman, “An Improved Approach for Sentiment Analysis of Arabic Tweets in Twitter Social Media,†in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), Mar. 2020, pp. 1–4, doi: 10.1109/ICCAIS48893.2020.9096850.
[7] J. Samuel, G. G. M. N. Ali, M. M. Rahman, E. Esawi, and Y. Samuel, “COVID-19 public sentiment insights and machine learning for tweets classification,†Information (Switzerland), vol. 11, no. 6, pp. 1–22, 2020, doi: 10.3390/info11060314.
[8] M. Alshaikh and M. Zohdy, “Sentiment Analysis for Smartphone Operating System: Privacy and Security on Twitter Data,†in 2020 IEEE International Conference on Electro Information Technology (EIT), Jul. 2020, pp. 366–369, doi: 10.1109/EIT48999.2020.9208303.
[9] I. C. Sari and Y. Ruldeviyani, “Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers,†in 2020 International Workshop on Big Data and Information Security (IWBIS), Oct. 2020, pp. 23–28, doi: 10.1109/IWBIS50925.2020.9255531.
[10] L. Mandloi and R. Patel, “Twitter Sentiments Analysis Using Machine Learninig Methods,†in 2020 International Conference for Emerging Technology (INCET), Jun. 2020, pp. 1–5, doi: 10.1109/INCET49848.2020.9154183.
[11] Kowsari, Jafari Meimandi, Heidarysafa, Mendu, Barnes, and Brown, “Text Classification Algorithms: A Survey,†Information, vol. 10, no. 4, pp. 1–68, Apr. 2019, doi: 10.3390/info10040150.
[12] A. H. Alamoodi et al., “Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review,†Expert Systems with Applications, vol. 167, no. April, pp. 1–13, Apr. 2021, doi: 10.1016/j.eswa.2020.114155.
[13] J. Samuel, M. M. Rahman, G. G. M. N. Ali, Y. Samuel, and A. Pelaez, “Feeling Like it is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics,†SSRN Electronic Journal, no. May, 2020, doi: 10.2139/ssrn.3604802.
[14] E. D. Sri Mulyani, D. Rohpandi, and F. A. Rahman, “Analysis Of Twitter Sentiment Using The Classification Of Naive Bayes Method About Television In Indonesia,†in 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), Aug. 2019, pp. 89–93, doi: 10.1109/ICORIS.2019.8874896.
[15] V. L. Nguyen, D. Kim, V. P. Ho, and Y. Lim, “A new recognition method for visualizing music emotion,†International Journal of Electrical and Computer Engineering, vol. 7, no. 3, pp. 1246–1254, 2017, doi: 10.11591/ijece.v7i3.pp1246-1254.
[16] M. A. Iftikar and Y. Sibaroni, “Analisis Sentimen Twitter: Penanganan Pandemi Covid-19 Menggunakan Metode Hybrid Naïve Bayes, Decision Tree, dan Support Vector Machine,†in e-Proceeding of Engineering, 2022, pp. 1809–1816.
[17] F. Sidik, I. Suhada, A. H. Anwar, and F. N. Hasan, “Analisis Sentimen Terhadap Pembelajaran Daring dengan Algoritma Naïve Bayes Classifier,†Jurnal Linguistik Komputasional, vol. 5, no. 1, pp. 34–43, 2022.
[18] F. Al-Ishafani and R. Mubarok, “Analisis Sentimen Pengguna Twitter Terhadap Kebijakan Pemberlakuan Pembatasan Sosial Berskala Besar (Psbb) Dengan Metode Naïve Bayes,†Jurnal Siliwangi, vol. 7, no. 1, pp. 19–24, 2021.
[19] S. Hadianti et al., “Analisis Sentiment Covid-19 Di Twitter Menggunakan Metode Naive Bayes Dan SVM,†Jurnal Teknologi Informasi, vol. 6, no. 1, pp. 58–63, 2022.
[20] 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, Aug. 2020, doi: 10.33480/inti.v15i1.1347.
[21] S. Efendi and P. Sihombing, “Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 3, pp. 583–594, Jul. 2022, doi: 10.30812/matrik.v21i3.1898.
[2] S. Supangat and M. Bin Saringat, “Development of E-Learning System Using Felder and Silverman’s Index of Learning Styles Model,†International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 5, pp. 8554–8561, 2020, doi: 10.30534/ijatcse/2020/236952020.
[3] M. Ardan, F. F. Rahman, and G. B. Geroda, “The influence of physical distance to student anxiety on COVID-19, Indonesia,†Journal of Critical Reviews, vol. 7, no. 17, pp. 1126–1132, 2020, doi: 10.31838/jcr.07.17.141.
[4] A. Faesal, A. Muslim, A. H. Ruger, and K. Kusrini, “Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 2, pp. 207–213, May 2020, doi: 10.30812/matrik.v19i2.640.
[5] H. H. Hidayat, A. Ardiansyah, P. Arsil, and L. I. Rahmawati, “Pemetaan Kata Kunci dan Polaritas Sentimen Pengguna Twitter Terhadap Kehalalan Produk,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 1–10, Nov. 2021, doi: 10.30812/matrik.v21i1.1162.
[6] H. AlSalman, “An Improved Approach for Sentiment Analysis of Arabic Tweets in Twitter Social Media,†in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), Mar. 2020, pp. 1–4, doi: 10.1109/ICCAIS48893.2020.9096850.
[7] J. Samuel, G. G. M. N. Ali, M. M. Rahman, E. Esawi, and Y. Samuel, “COVID-19 public sentiment insights and machine learning for tweets classification,†Information (Switzerland), vol. 11, no. 6, pp. 1–22, 2020, doi: 10.3390/info11060314.
[8] M. Alshaikh and M. Zohdy, “Sentiment Analysis for Smartphone Operating System: Privacy and Security on Twitter Data,†in 2020 IEEE International Conference on Electro Information Technology (EIT), Jul. 2020, pp. 366–369, doi: 10.1109/EIT48999.2020.9208303.
[9] I. C. Sari and Y. Ruldeviyani, “Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers,†in 2020 International Workshop on Big Data and Information Security (IWBIS), Oct. 2020, pp. 23–28, doi: 10.1109/IWBIS50925.2020.9255531.
[10] L. Mandloi and R. Patel, “Twitter Sentiments Analysis Using Machine Learninig Methods,†in 2020 International Conference for Emerging Technology (INCET), Jun. 2020, pp. 1–5, doi: 10.1109/INCET49848.2020.9154183.
[11] Kowsari, Jafari Meimandi, Heidarysafa, Mendu, Barnes, and Brown, “Text Classification Algorithms: A Survey,†Information, vol. 10, no. 4, pp. 1–68, Apr. 2019, doi: 10.3390/info10040150.
[12] A. H. Alamoodi et al., “Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review,†Expert Systems with Applications, vol. 167, no. April, pp. 1–13, Apr. 2021, doi: 10.1016/j.eswa.2020.114155.
[13] J. Samuel, M. M. Rahman, G. G. M. N. Ali, Y. Samuel, and A. Pelaez, “Feeling Like it is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics,†SSRN Electronic Journal, no. May, 2020, doi: 10.2139/ssrn.3604802.
[14] E. D. Sri Mulyani, D. Rohpandi, and F. A. Rahman, “Analysis Of Twitter Sentiment Using The Classification Of Naive Bayes Method About Television In Indonesia,†in 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), Aug. 2019, pp. 89–93, doi: 10.1109/ICORIS.2019.8874896.
[15] V. L. Nguyen, D. Kim, V. P. Ho, and Y. Lim, “A new recognition method for visualizing music emotion,†International Journal of Electrical and Computer Engineering, vol. 7, no. 3, pp. 1246–1254, 2017, doi: 10.11591/ijece.v7i3.pp1246-1254.
[16] M. A. Iftikar and Y. Sibaroni, “Analisis Sentimen Twitter: Penanganan Pandemi Covid-19 Menggunakan Metode Hybrid Naïve Bayes, Decision Tree, dan Support Vector Machine,†in e-Proceeding of Engineering, 2022, pp. 1809–1816.
[17] F. Sidik, I. Suhada, A. H. Anwar, and F. N. Hasan, “Analisis Sentimen Terhadap Pembelajaran Daring dengan Algoritma Naïve Bayes Classifier,†Jurnal Linguistik Komputasional, vol. 5, no. 1, pp. 34–43, 2022.
[18] F. Al-Ishafani and R. Mubarok, “Analisis Sentimen Pengguna Twitter Terhadap Kebijakan Pemberlakuan Pembatasan Sosial Berskala Besar (Psbb) Dengan Metode Naïve Bayes,†Jurnal Siliwangi, vol. 7, no. 1, pp. 19–24, 2021.
[19] S. Hadianti et al., “Analisis Sentiment Covid-19 Di Twitter Menggunakan Metode Naive Bayes Dan SVM,†Jurnal Teknologi Informasi, vol. 6, no. 1, pp. 58–63, 2022.
[20] 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, Aug. 2020, doi: 10.33480/inti.v15i1.1347.
[21] S. Efendi and P. Sihombing, “Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 3, pp. 583–594, Jul. 2022, doi: 10.30812/matrik.v21i3.1898.
Downloads
Published
2023-03-31
Issue
Section
Articles
How to Cite
[1]
S. Supangat, M. Z. B. Saringat, and M. Y. F. Rochman, “Predicting Handling Covid-19 Opinion using Naive Bayes and TF-IDF for Polarity Detection”, MATRIK, vol. 22, no. 2, pp. 173–184, Mar. 2023, doi: 10.30812/matrik.v22i2.2227.
Similar Articles
- Fitra Ahya Mubarok, Mohammad Reza Faisal, Dwi Kartini, Dodon Turianto Nugrahadi, Triando Hamonangan Saragih, Gender Classification of Twitter Users Using Convolutional Neural Network , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 1 (2023)
- Hermila A., Rahmat Taufik R. L Bau, Sitti Suhada, Abdulaziz Ahmed siyad, Predicting Gen Z’s Sentiments on Gorontalo’s CulturalWisdom UsingSentiment Analysis Models , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 2 (2025)
- Hety Handayani Hidayat, Ardiansyah Ardiansyah, Poppy Arsil, Laras Isna Rahmawati, Pemetaan Kata Kunci dan Polaritas Sentimen Pengguna Twitter Terhadap Kehalalan Produk , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 1 (2021)
- Frans Mikael Sinaga, Sio Jurnalis Pipin, Sunaryo Winardi, Karina Mannita Tarigan, Ananda Putra Brahmana, Analyzing Sentiment with Self-Organizing Map and Long Short-Term Memory Algorithms , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 1 (2023)
- Andris Faesal, Aziz Muslim, Aditya Hastami Ruger, Kusrini Kusrini, Sentimen Analisis pada Data Tweet Pengguna Twitter Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 19 No. 2 (2020)
- Christofer Satria, Peter Wijaya Sugijanto, Anthony Anggrawan, I Nyoman Yoga Sumadewa, Aprilia Dwi Dayani, Rini Anggriani, Multi-Algorithm Approach to Enhancing Social Assistance Efficiency Through Accurate Poverty Classification , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 1 (2024)
- M. Khairul Anam, Bunga Nanti Pikir, Muhammad Bambang Firdaus, Susi Erlinda, Agustin Agustin, Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen dan Pemeritah , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 1 (2021)
- Putu Tisna Putra, Anthony Anggrawan, Hairani Hairani, Comparison of Machine Learning Methods for Classifying User Satisfaction Opinions of the PeduliLindungi Application , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 3 (2023)
- Winny purbaratri, Hindriyanto Dwi Purnomo, Danny Manongga, Iwan Setyawan, Hendry Hendry, Sentiment Analysis of e-Government Service Using the Naive Bayes Algorithm , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 2 (2024)
- Rizky Afrinanda, Lusiana Efrizoni, Wirta Agustin, Rahmiati Rahmiati, Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 2 (2023)
1-10 of 155
Next
You may also start an advanced similarity search for this article.
.png)
