Version 1
: Received: 29 January 2024 / Approved: 29 January 2024 / Online: 29 January 2024 (15:42:52 CET)
How to cite:
Thakur, N.; Patel, K.A.; Poon, A.; Shah, R.; Azizi, N.; Han, C. Investigating the Global Fear associated with COVID-19 using Subjectivity Analysis and Deep Learning. Preprints2024, 2024012023. https://doi.org/10.20944/preprints202401.2023.v1
Thakur, N.; Patel, K.A.; Poon, A.; Shah, R.; Azizi, N.; Han, C. Investigating the Global Fear associated with COVID-19 using Subjectivity Analysis and Deep Learning. Preprints 2024, 2024012023. https://doi.org/10.20944/preprints202401.2023.v1
Thakur, N.; Patel, K.A.; Poon, A.; Shah, R.; Azizi, N.; Han, C. Investigating the Global Fear associated with COVID-19 using Subjectivity Analysis and Deep Learning. Preprints2024, 2024012023. https://doi.org/10.20944/preprints202401.2023.v1
APA Style
Thakur, N., Patel, K.A., Poon, A., Shah, R., Azizi, N., & Han, C. (2024). Investigating the Global Fear associated with COVID-19 using Subjectivity Analysis and Deep Learning. Preprints. https://doi.org/10.20944/preprints202401.2023.v1
Chicago/Turabian Style
Thakur, N., Nazif Azizi and Changhee Han. 2024 "Investigating the Global Fear associated with COVID-19 using Subjectivity Analysis and Deep Learning" Preprints. https://doi.org/10.20944/preprints202401.2023.v1
Abstract
The work presented in this paper makes multiple scientific contributions related to the investigation of the global fear associated with COVID-19 by performing a comprehensive analysis of a dataset comprising survey responses of participants from 40 countries. First, the results of subjectivity analysis of responses where participants indicated their biggest concern related to COVID-19 showed that the average subjectivity in responses by the age group of 41-50 decreased from April 2020 to June 2020, the average subjectivity in responses by the age group of 71-80 drastically increased from May 2020, and the age group of 11-20 indicated the least level of subjectivity in their responses between June 2020 to August 2020. Second, subjectivity analysis also revealed the percentage of highly opinionated, neutral opinionated, and least opinionated responses per age-group where the analyzed age groups were 11-20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, and 81-90. For instance, the percentage of highly opinionated, neutral opinionated, and least opinionated responses by the age group of 11-20 were 17.92%, 16.24%, and 65.84%, respectively. Third, data analysis of responses from different age groups showed that the highest percentage of responses indicating that they were very worried about COVID-19 came from individuals in the age group of 21-30. Fourth, data analysis of the survey responses also revealed that in the context of taking precautions to prevent contracting COVID-19, the percentage of individuals in the age group of 31-40 taking precautions was higher as compared to the percentages of individuals from the age groups of 41-50, 51-60, 61-70, 71-80, and 81-90. Finally, a deep learning model was developed to detect if the survey respondents were seeing or planning to see a psychologist or psychiatrist for any mental health issues related to COVID-19. The deep learning model used the responses to multiple questions in the context of fear, preparedness, and response related to COVID-19 from the dataset and achieved an overall performance accuracy of 91.62% after 500 epochs.
Keywords
COVID-19; Big Data; Data Analysis; Machine Learning; Subjectivity Analysis; Data Science; Deep Learning; Mental Health
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
