Document Type : Articles
Authors
Atma Jaya Yogyakarta University
Abstract
The spread of COVID-19 has recently become a public concern. There are many public emotions regarding implementing the Large-Scale Social Restrictions (PSBB), which was especially implemented in Jakarta, first implemented in Indonesia. People have various emotions mirroring their tweets in making statements on social media, especially Twitter. Emotional expressions can be joy, sadness, anger, and fear. This study aims to determine the impact of the implementation of PSBB in reducing the spread of COVID-19 on people's emotional factors on Twitter. The method used in this research is the SentiStrength method and Support Vector Machine. Furthermore, the comparison between the two methods is completed to determine which one is better. The tweet data used were 12,735 lines from 10 April 2020 to 21 August 2020. The highest accuracy achieved of SentiStrength and SVM is 88.33% and 73.33%, respectively. Similarly, f-measure of SentiStrength (88.14%) outperforms SVM (75%). This research shows that the implementation of PSBB on public emotional factors on Twitter is that happy emotions with the highest sentiment are positive sentiments, reaching 5246 sentiments.https://dorl.net/dor/20.1001.1.20088302.2022.20.2.1.6
Keywords
- Bouazizi, M., & Ohtsuki, T. (2019). Multi-Class Sentiment Analysis on Twitter: Classification Performance and Challenges. Big Data Mining and Analytics, 2(3), 181–194. https://doi.org/10.26599/bdma.2019.9020002
- Debortoli, S., Müller, O., Junglas, I., & vom Brocke, J. (2016). Text mining for information systems researchers: An annotated topic modeling tutorial. Communications of the Association for Information Systems, 39(1), 110–135. https://doi.org/10.17705/1cais.03907
- Ferrara, E., & Yang, Z. (2015). Measuring Emotional Contagion in Social Media. PLOS ONE, 10(11), 1–14. https://doi.org/10.1371/journal.pone.0142390
- Franco-Riquelme, J. N., Bello-Garcia, A., & Ordieres-Mere, J. (2019). Indicator Proposal for Measuring Regional Political Support for the Electoral Process on Twitter: The Case of Spain’s 2015 and 2016 General Elections. IEEE Access, 7, 62545–62560. https://doi.org/10.1109/ACCESS.2019.2917398
- Gugus Tugas Percepatan Penanganan COVID-19. (2020). Peta Sebaran. Gugus Tugas Percepatan Penanganan COVID-19. https://covid19.go.id/peta-sebaran
- Kementrian Kesehatan Republik Indonesia. (2020). Pembatasan Sosial Berskala Besar PSBB. Kementrian Kesehatan Republik Indonesia. https://www.kemkes.go.id/
- McAuley, J., Pandey, R., & Leskovec, J. (2015). Inferring Networks of Substitutable and Complementary Products. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pages. https://doi.org/http://dx.doi.org/10.1145/2783258.2783381
- McAuley, J., Targett, C., Shi Javen, Q., & Hengel Van Den, A. (2015). Image-based Recommendations on Styles and Substitutes. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/http://dx.doi.org/10.1145/2766462.2767755
- Mohamed Shakeel, P., & Baskar, S. (2020). Automatic human emotion classification in web document using fuzzy inference system (FIS): Human emotion classification. International Journal of Technology and Human Interaction, 16(1), 94–104. https://doi.org/10.4018/IJTHI.2020010107
- Muhammad, W., Mushtaq, M., Junejo, K. N., & Khan, M. Y. (2020). Sentiment Analysis of Product Reviews in the Absence of Labelled Data Using Supervised Learning Approaches. Malaysian Journal of Computer Science, 33(2), 118–132. https://doi.org/10.22452/mjcs.vol33no2.3
- Nagarajan, S. M., & Gandhi, U. D. (2018). Classifying streaming of Twitter data based on sentiment analysis using hybridization. Neural Computing and Applications, 31(5), 1425–1433. https://doi.org/10.1007/s00521-018-3476-3
- Pagolu, V. S., Reddy, K. N., Panda, G., & Majhi, B. (2016). Sentiment Analysis of Twitter Data for Predicting Stock Market Movements. International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016. https://doi.org/10.1109/SCOPES.2016.7955659
- Putra, F. D. N., Pranowo, & Setyohadi, B. (2020). Sentiment analysis of Indonesian presisential election 2019 on the twitter with lexicon-based and support vector machine (SVM). AIP Conference Proceedings, 2217, 030136–1–030136–030138. https://doi.org/10.1063/5.0000631
- Rezwanul, M., Ali, A., & Rahman, A. (2017). Sentiment Analysis on Twitter Data using KNN and SVM. International Journal of Advanced Computer Science and Applications, 8(6), 19–25. https://doi.org/10.14569/ijacsa.2017.080603
- Troussas, C., Virvou, M., Espinosa, K. J., Llaguno, K., & Caro, J. (2013). Sentiment analysis of Facebook statuses using Naive Bayes Classifier for language learning. 4th International Conference on Information, Intelligence, Systems and Applications, 198–205. https://doi.org/10.1109/IISA.2013.6623713
- Tseng, C. W., Chou, J. J., & Tsai, Y. C. (2018). Text mining analysis of teaching evaluation questionnaires for the selection of outstanding teaching faculty members. IEEE Access, 6, 72870–72879. https://doi.org/10.1109/ACCESS.2018.2878478
- WHO. (2020). Global update on coronavirus disease. WHO. https://www.who.int/indonesia/news/novel-coronavirus
- Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X. (2019). Sentiment analysis of comment texts based on BiLSTM. IEEE Access, 7(c), 51522–51532. https://doi.org/10.1109/ACCESS.2019.2909919
', './data/ijism/coversheet/cover_en.jpg'))