The World Health Organization (WHO) declared the COVID-19 outbreak has resulted in more than six million confirmed cases and more than 371,000 deaths globally on June 1, 2020. The incident sparked a flood of scientific research to help society deal with the virus, both inside and outside the medical domain. Research related to public health analysis and public conversations about the spread of COVID-19 on social media is one of the highlights of researchers in the world. People can analyze information from social media as supporting data about public health. Analyzing public conversations will help the relevant authorities understand public opinion and information gaps between them and the public, helping them develop appropriate emergency response strategies to address existing problems in the community during the pandemic and provide information on the population's emotions in different contexts. However, research related to the analysis of public health and public conversations was so far conducted only through supervised analysis of textual data. In this study, we aim to analyze specifically the sentiment and topic modeling of Indonesian public conversations about the COVID-19 on Twitter using the NLP technique. We applied some methods to analyze the sentiment to obtain the best classification method. In this study, the topic modeling was carried out unsupervised using Latent Dirichlet Allocation (LDA). The results of this study reveal that the most frequently discussed topic related to the COVID-19 pandemic is economic issues.
World Health Organization (WHO), “The Coronavirus Disease 2019 (COVID-19) Situation Report.,” WHO, 2020. .
WHO, “Coronavirus disease (COVID-19) Situation Report – 133,” 2020.
M. Haghani, M. C. J. Bliemer, F. Goerlandt, and J. Li, “The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review,” Saf. Sci., vol. 129, p. 104806, Sep. 2020.
X. Pan, L. Dong, N. Yang, D. Chen, and C. Peng, “Potential drugs for the treatment of the novel coronavirus pneumonia (COVID-19) in China,” Virus Res., vol. 286, p. 198057, Jun. 2020.
L. Dong, S. Hu, and J. Gao, “Discovering drugs to treat coronavirus disease 2019 (COVID-19),” Drug Discov. Ther., vol. 14, no. 1, pp. 58–60, Feb. 2020.
D. H. Lee, S. Kim, J. S. Kim, B. G. Kim, K. H. Chang, and J. O. Park, “Emergent procedures for oropharyngeal bleeding during the COVID-19 pandemic: Protection of medical staff,” American Journal of Otolaryngology - Head and Neck Medicine and Surgery, vol. 41, no. 5. W.B. Saunders, p. 102583, Sep-2020.
Y. Shi et al., “Knowledge and attitudes of medical staff in Chinese psychiatric hospitals regarding COVID-19,” Brain, Behav. Immun. - Heal., vol. 4, p. 100064, Apr. 2020.
J. Wang, M. Kuang, L. Chen, C. Lian, L. Zhao, and S. Wang, “Strategy for treating vascular emergencies during the COVID-19 pandemic in China,” J. Vasc. Surg., Jun. 2020.
A. K. Singh, R. Gupta, A. Ghosh, and A. Misra, “Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 4, pp. 303–310, Jul. 2020.
R. Gupta, A. Ghosh, A. K. Singh, and A. Misra, “Clinical considerations for patients with diabetes in times of COVID-19 epidemic,” Diabetes and Metabolic Syndrome: Clinical Research and Reviews, vol. 14, no. 3. Elsevier Ltd, pp. 211–212, May-2020.
Y. Chen, X. Gong, L. Wang, and J. Guo, “Effects of hypertension, diabetes and coronary heart disease on COVID-19 diseases severity: a systematic review and meta-analysis,” medRxiv, no. 280, p. 2020.03.25.20043133, Mar. 2020.
X. Chen et al., “Hypertension and Diabetes Delay the Viral Clearance in COVID-19 Patients,” Cold Spring Harbor Laboratory Press, Mar. 2020.
B. N. Ashraf, “Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets,” J. Behav. Exp. Financ., p. 100371, Jun. 2020.
T. Laing, “The economic impact of the Coronavirus 2019 (Covid-2019): Implications for the mining industry,” Extractive Industries and Society, vol. 7, no. 2. Elsevier Ltd, pp. 580–582, Apr-2020.
M. Sigala, “Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research,” J. Bus. Res., vol. 117, pp. 312–321, Sep. 2020.
L. H. N. Fong, R. Law, and B. H. Ye, “Outlook of tourism recovery amid an epidemic: Importance of outbreak control by the government,” Ann. Tour. Res., p. 102951, May 2020.
A. Jarynowski, M. Wojta-Kempa, and V. Belik, “Trends in Perception of COVID-19 in Polish Internet,” medRxiv, p. 2020.05.04.20090993, May 2020.
H. J. Larson, “The biggest pandemic risk? Viral misinformation,” Nature, vol. 562, no. 7726, pp. 309–310, Oct. 2018.
D. Allington, B. Duffy, S. Wessely, N. Dhavan, and J. Rubin, “Health-protective behaviour, social media usage, and conspiracy belief during the COVID-19 public health emergency,” Psychol. Med., pp. 1–7, 2020.
L. Li et al., “Characterizing the Propagation of Situational Information in Social Media during COVID-19 Epidemic: A Case Study on Weibo,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 2, pp. 556–562, Apr. 2020.
L. (Lucy) Yan and A. J. Pedraza‐Martinez, “Social Media for Disaster Management: Operational Value of the Social Conversation,” Prod. Oper. Manag., vol. 28, no. 10, pp. 2514–2532, Oct. 2019.
M. Minicucci Ibañez, R. R. Rosa, and L. N. F. Guimarães, “Sentiment Analysis Applied to Analyze Society’s Emotion in Two Different Context of Social Media Data,” Intel. Artif., vol. 23, no. 66, pp. 66–84, Dec. 2020.
R. F. Sear et al., “Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning,” IEEE Access, vol. 8, pp. 91886–91893, 2020.
H. W. Park, S. Park, and M. Chong, “Conversations and medical news frames on twitter: Infodemiological study on COVID-19 in South Korea,” J. Med. Internet Res., vol. 22, no. 5, p. e18897, May 2020.
M. O. Lwin et al., “Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends.,” JMIR public Heal. Surveill., vol. 6, no. 2, p. e19447, May 2020.
H. Jelodar, Y. Wang, R. Orji, and H. Huang, “Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach,” IEEE J. Biomed. Heal. Informatics, pp. 1–1, Jun. 2020.
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,” Inf., vol. 11, no. 6, pp. 1–23, 2020.
“Kasus Covid-19 Pertama, Masyarakat Jangan Panik | Indonesia.go.id.” [Online]. Available: https://indonesia.go.id/narasi/indonesia-dalam-angka/ekonomi/kasus-covid-19-pertama-masyarakat-jangan-panik. [Accessed: 17-Jan-2021].
B. Liu, Sentiment Analysis and Opinion Mining, no. May. Morgan & Claypool Publishers, 2012.
B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Found. Trends Inf. Retr., vol. 2, no. 1, 2008.
A. Pak and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining.” 2010.
A. Rane and A. Kumar, “Sentiment Classification System of Twitter Data for US Airline Service Analysis,” in Proceedings - International Computer Software and Applications Conference, 2018, vol. 1, pp. 769–773.
S. G. Kanakaraddi, A. K. Chikaraddi, K. C. Gull, and P. S. Hiremath, “Comparison Study of Sentiment Analysis of Tweets using Various Machine Learning Algorithms,” in Proceedings of the 5th International Conference on Inventive Computation Technologies, ICICT 2020, 2020, pp. 287–292.
D. M. Blei, “Probabilistic topic models,” Commun. ACM, vol. 55, no. 4, pp. 77–84, Apr. 2012.
I. Vayansky and S. A. P. Kumar, “A review of topic modeling methods,” Inf. Syst., vol. 94, p. 101582, Dec. 2020.
Y. Hu, J. Boyd-Graber, B. Satinoff, and A. Smith, “Interactive topic modeling,” Mach. Learn., vol. 95, no. 3, pp. 423–469, Oct. 2014.
Y. Zuo et al., “Topic modeling of short texts: A pseudo-document view,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, vol. 13-17-August-2016, pp. 2105–2114.
H. Jelodar et al., “Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey,” Multimed. Tools Appl., vol. 78, no. 11, pp. 15169–15211, Jun. 2019.
D. Newman, H. Jey, Lau, K. Grieser, and T. Baldwin, “Automatic Evaluation of Topic Coherence,” 2010.
A. A. Anees, H. Prakash Gupta, A. P. Dalvi, S. Gopinath, and B. R. Mohan, “Performance analysis of multiple classifiers using different term weighting schemes for sentiment analysis,” in 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019, 2019, pp. 637–641.
J. U. Kundale and N. J. Kulkarni, “Language independent multi-class sentiment analysis,” in Proceedings - 2019 5th International Conference on Computing, Communication Control and Automation, ICCUBEA 2019, 2019.
A. Yadav, C. K. Jha, A. Sharan, and V. Vaish, “Sentiment analysis of financial news using unsupervised approach,” in Procedia Computer Science, 2020, vol. 167, pp. 589–598.
A. M. Rahat, A. Kahir, and A. K. M. Masum, “Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset,” 2020, pp. 266–270.
P. Pankaj, P. Pandey, M. Muskan, and N. Soni, “Sentiment Analysis on Customer Feedback Data: Amazon Product Reviews,” in Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, 2019, pp. 320–322.
A. Rahman and M. S. Hossen, “Sentiment Analysis on Movie Review Data Using Machine Learning Approach,” in 2019 International Conference on Bangla Speech and Language Processing, ICBSLP 2019, 2019.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2021 IJID (International Journal on Informatics for Development)