HomeQSU Research Journalvol. 9 no. 1 (2020)


Melidiossa V. Pagudpud

Discipline: Computer Science



COVID-19 has a significant impact on the economic, social, and health well-being of people all over the world. This article examines public attitude about the COVID-19 outbreak, notably in Cagayan Valley, Philippines. The Mallet topic modeling tool set was used to apply the Latent Dirichlet Allocation (LDA) Approach to topic modeling, a machine learning technique. The results reveal that topic modeling using the hyperparameters of number of topics (k) = 5, number of iterations = 1000, number of words = 10, and optimization interval (Alpha) = 10 gave a precision of 100%, indicating that the created cluster of words agreed with the human assessment. Similarly, the study found that public sentiment in Cagayan Valley was concentrated on five (5) latent topics: (a) COVID19 testing and case reporting; (b) relief operations; (c) contact tracing; (d) government programs addressing COVID-19; and (e) community quarantine. This examination of public sentiments in the region regarding the COVID-19 pandemic indicates the aspects on which public worries concentrated during the pandemic's struggle. This is important for the government to have a clearer and more comprehensive picture of how the region's provincial government entities can fairly respond to the pandemic.


  1. Cuaton, G., Caluza, L. J. B., & Neo, J. B. (2020). COVID-19 Health Response from January to April 2020 in the Philippines: A Topic Modeling Analysis using Latent Dirichlet Allocation Algorithm. SSRN Electronic Journal. DOI:10.2139/ssrn.3590910
  2. Gormley, M. (2016). Dirichlet Process and Dirichlet Process Mixtures. Probabilistic Graphical Models 10-708http://www.cs.cmu.edu/~epxing/Class/10708-16/note/10708 _scribe_lecture18.pdf
  3. Hansen, J. (2016). Inside Latent Dirichlet Allocation: An Empirical Exploration. DOI: 10.13140/RG.2.2.30231.37287.
  4. Islam, M. S., Sarkar, T., Khan, S. H., Mostofa Kamal, A. H., Hasan, S. M. M., Kabir, A., Yeasmin, D., Islam, M. A., Amin Chowdhury, K. I., Anwar, K. S., Chughtai, A. A., & Seale, H. (2020). COVID-19-Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis. The American journal of tropical medicine and hygiene, 103(4), 1621–1629. https://doi.org/10.4269/ajtmh.20-0812
  5. Na, L., Di, T., Ying, L., Xiao-Jun, T., & Hai-Wen, W. (2015). Topic-Sensitive Multi-document Summarization Algorithm. Computer Science and Information Systems, 12, 60-60. DOI: 10.2298/CSIS140815060N.
  6. Official Gazette of the Philippines. (2020). Data Privacy Act of 2012https://www.officialgazette.gov.ph/2012/ 08/15/republic-act-no-10173/
  7. Peter, C., Rossmann, C., & Keyling, T. (2014). Exemplification 2.0: Roles of direct and indirect social information in conveying health messages through social network sites. Journal of Media Psychology: Theories, Methods, and Applications, 26(1), 19–28. https://doi.org/10.1027/1864-1105/a000103
  8. Sabbagh, R., & Ameri, F. (2020). A Framework Based on K-means Clustering and Topic Modeling for Analyzing Unstructured Manufacturing Capability Data. Journal of Computing and Information Science in Engineering, 20. DOI: 10.1115/1.4044506.
  9. Vijayarani, S., Ilamathi, J., & Nithya, M. (2015). Preprocessing Techniques for Text Mining - An Overview. International Journal of Computer Science and Communication Networks, 1(5), 7–16.
  10. World Health Organization (WHO). (2020). Naming the coronavirus disease (COVID19) and the virus that causes ithttps://www.who.int/emergencies/diseases/novel-coronavirus-2019/technicalguidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it.