Sentiment-Topic Dynamics on COVID-19 Post-Traumatic Stress Disorder using Online Published Researches

  • Guilbert Nicanor A Atillo

Abstract

This paper will analyze the COVID-19 Posttraumatic Stress Disorder (PSTD) researches retrieved from PubMed and other online sources to determine the impact of the COVID-19 pandemic on mental health leading to PSTD. The Sentiment Classifier and Latent Dirichlet Allocation (LDA) were used to detect the topic relevant to post-traumatic stress disorder. The finding revealed a negative sentiment while the use of Latent Dirichlet Allocation determined the critical issue “COVID”. The results suggest further study of the stress and other traumatic experiences bought by the COVID.

References

Abdelghani, M., Hassan, M.S., Alsadik, M.E. et al. (2021). Post-traumatic stress symptoms
among an Egyptian sample of post-remission COVID-19 survivors: prevalence and
sociodemographic and clinical correlates. Middle East Curr Psychiatry 28, 20.
Anand Babu, G.L., & Srinivasu, B. (2019). A Conceptual Based Approach in Text Mining:
Techniques and Applications. International Journal of Innovative Technology and
Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-7, 1779.
Ananiadou, S., Kell, D. B., & Tsujii, J. I. (2006). Text mining and its potential applications in
systems biology. Trends in Biotechnology, 24(12), 571-579.
Antons, D., Grünwald, E., Cichy, P., & Salge, T. O. (2020). The application of text mining
methods in innovation research: current state, evolution patterns, and development
priorities. R&D Management, 50(3), 329-351.
Aslam, F., Awan, T., Syed, J., Kashif, A., & Parveen, M. (2020). Sentiments and emotions
evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanities and Social Sciences Communications, vol. 7, no. 1.
Australian Bureau of Statistics. (2018). National survey of mental health and well-being:
Summary of results. Canberra, ACT: ABS. Available at
www.abs.gov.au/statistics/health/mental-health/national-survey-mental-health andwellbeing-summary-results/latest-release [Accessed 11 June 2021].
Basu, T. (2020). The coronavirus pandemic is a game-changer for mental health care. MIT
Technology Review.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. The Journal of
Machine Learning Research, 3, 993-1022.
Boon-Itt, S. (2020). Public Perception of the COVID-19 Pandemic on Twitter: Sentiment
Analysis and Topic Modeling Study. JMIR Public Health Surveill. 6(4):e21978.
Chehal, D., Gupta, P., & Gulati, P. (2020). COVID-19 pandemic lockdown: An emotional
health perspective of Indians on Twitter. International Journal of Social Psychiatry, p.002076402094074.
Chen, X., Lun, Y., Yan, J. et al. (2019). Discovering thematic change and evolution of
utilizing social media for healthcare research. BMC Med Inform Decis Mak 19, 50.
Cheng, X., Yan, X., Lan, Y., Guo. J. (20140. BTM: Topic modeling over short texts. IEEE
Transactions on Knowledge and Data Engineering. 26:1–1.
Cooper, J., Phelps, A. J., Ng, C. H., & Forbes, D. (2020). Diagnosis and treatment of posttraumatic stress disorder during the COVID-19 pandemic. Australian Journal of general practice, 49(12), 785-789.
Creamer, M., Burgess, P., & McFarlane, A. (2001). Post-traumatic stress disorder: Findings
from the Australian national survey of mental health and well-being. Psychol Med,
31(7):1237–47.
De Caro W. (2020). Infodemia and COVID-19: a text mining analysis. The European Journal
of Public Health, 30(Suppl 5), ckaa165.065.
Faiiazee, H., Al-Haddad, S.A. R., Abdullah, R., & Samsudin, K. (2012). Text mining in bioinformatics: Past, present, and future. 2012 International Conference on Information Retrieval & Knowledge Management, Kuala Lumpur, pp. 327-330.
Feldman, R., & Sanger, J. (2007). The text mining handbook: Advanced approaches in
analyzing unstructured data. Cambridge: Cambridge University Press.
Garcia, K., & Berton, L. (2021). Topic detection and sentiment analysis in Twitter content
related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101, 107057
Gong, L. (2018). Application of biomedical text mining. Artificial Intelligence: Emerging
Trends and Applications, 417.
Gupta, A., Dengre, V., Kheruwala, H.A. et al. (2020). Comprehensive review of text-mining
applications in finance. Financ Innov 6, 39.
He, W. (2013). Examining student's online interaction in a live video streaming environment
using data mining and text mining. Computers in Human Behavior, vol. 29, no. 1, pp.
90–102, 2013.
Hong, J. W., & Park, S. B. (2019). The Identification of Marketing Performance Using Text
Mining of Airline Review Data. Mobile Information Systems, 2019.
Kim, Y. M., & Delen, D. (2018). Medical informatics research trend analysis: A text mining
approach. Health informatics journal, 24(4), 432-452.
Komasawa, N., Terasaki, F., Nakano, T., Saura, R., & Kawata, R. (2020). A text mining
analysis of perceptions of the COVID-19 pandemic among final-year medical students. Acute Medicine & Surgery. e576. 10.1002/ams2.576.
Li, N., & Wu, D. (2010). Using text mining and sentiment analysis for online forums hotspot
detection and forecast. Decision Support Systems, vol. 48, no. 2, pp. 354-368.
Medhat, W., Hassan, A., & Korashy, H. J.(2014). Sentiment analysis algorithms and
applications: A survey. Vol. 5, no. 4, pp. 1093-1113.
Moreno, C., Wykes, T., Galderisi, S., Nordentoft, M., Crossley, N., Jones, N., ... & Arango, C. (2020). How mental health care should change as a consequence of the COVID-19
pandemic—the Lancet Psychiatry.
Norambuena, B.K., Lettura, E., & Villegas, C. ( 2019). Sentiment analysis and opinion mining applied to scientific paper reviews. Intelligent Data Analysis, vol. 23, no. 1, pp. 191-214, 2019.
Panchal, N., Kamal, R., Orgera, K., Cox, C., Garfield, R., Hamel, L., & Chidambaram, P.
(2020). The implications of COVID-19 for mental health and substance use. Kaiser
family foundation. Chicago.
Preethi, B.M., & Radha, P. (2017). A Survey Paper on Text Mining - Techniques, Applications, And Issues. IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN:2278- 0661,p-ISSN: 2278-8727 PP 46-51.
Pekrun R., & Stephens S.J. (2010). Achievement emotions in higher education. Higher
Education: Handbook of Theory and Research, Volume 25. Springer; New York, NY,
USA: 2010. pp. 257–306.
Qi, Y. (2009). Text mining in bioinformatics: Research and application. In Handbook of
Research on Text and Web Mining Technologies (pp. 748-757). IGI Global.
Rowe, A. D., & Fitness, J. (2018). Understanding the role of negative emotions in adult
Learning and achievement: A social functional perspective. Behavioral sciences, 8(2), 27.
Saha, K., Torous, J., Caine, E.D., & De Choudhury, M. (2020). Psychosocial Effects of the
COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media. J Med
Internet Res, 22(11):e22600.
Talib, R., Hanif, M. K., Ayesha, S., & Fatima, F. (2016). Text mining: techniques,
applications, and issues. International Journal of Advanced Computer Science and
Applications, 7(11), 414-418.
Tucker, P., & Czapla, C. S. (2020). Post-COVID stress disorder: another emerging
consequence of the global pandemic. Psychiatric Times.
Tworowski, D., Gorohovski, A., Mukherjee, S., et al. (2021). COVID19 Drug Repository:
text-mining the literature in search of putative COVID19 therapeutics, Nucleic Acids
Research, Volume 49, Issue D1, Pages D1113–D1121.
Wang, C. C. N., Chang, I., Sheu, P. C. Y., & Tsai, J. J. P. "Application of Semantic
Computing in Cancer on Secondary Data Analysis," 2018 Second IEEE International
Conference on Robotic Computing (IRC), Laguna Hills, CA, 2018, pp. 407-412.
Xiao, S., Luo, D., & Xiao, Y. (2020). Survivors of COVID-19 are at high risk of posttraumatic stress disorder. Global Health Research And Policy, 5(1).
Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., & Zhu, T. (2020). Public discourse and
sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PLoS ONE, 15(9): e0239441.
Yoon, S., Elhadad, N., & Bakken, S. (2013). A practical approach for content mining of
tweets. American Journal of Preventive Medicine, 45(1), 122-129.
Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm
equity value: a sentiment analysis approach. Decision Support Systems, 55(4), 919- 926.
Zanini, N., & Dhawan, V. (2015). Text Mining: An introduction to theory and some
applications. Research Matters, 19, 38-45.
Zhang, C., Jiang, J., Jin, H., & Chen, T. (2021). The Impact of COVID-19 on Consumers
Psychological Behavior Based on Data Mining for Online User Comments in the
Catering Industry in China" Int. J. Environ. Res. Public Health 18, no. 8: 4178.
Zhong B, Jiang, Z, Xie, W, & Qin, X. (2020). Association of Social Media Use With Mental
Health Conditions of Nonpatients During the COVID-19 Outbreak: Insights from a
National Survey Study. J Med Internet Res, 22(12):e23696.
Zhu, F., Patumcharoenpol, P., Zhang, C., et al. (2013). Biomedical text mining and its
applications in cancer research. Journal of Biomedical Informatics, Volume 46, Issue 2, Pages 200.
Published
2021-12-01
How to Cite
A ATILLO, Guilbert Nicanor. Sentiment-Topic Dynamics on COVID-19 Post-Traumatic Stress Disorder using Online Published Researches. Asian Journal of Research in Education and Social Sciences, [S.l.], v. 3, n. 4, p. 28-35, dec. 2021. Available at: <https://myjms.mohe.gov.my/index.php/ajress/article/view/16329>. Date accessed: 10 aug. 2022.
Section
Articles