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Lookup NU author(s): Dr Husnain SheraziORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2024. The COVID-19 pandemic has disrupted people’s lives and caused significant economic damage around the world, but its impact on people’s mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD). The goal of this study is to use classical machine learning approaches to classify tweets into COVID-PTSD positive or negative categories. To this end, we employed various Machine Learning (ML) classifiers, to segregate the psychotic difficulties with the user’s PTSD in the context of COVID-19, including Random Forest Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor. ML models are trained and tested using various combinations of feature selection strategies to get the best possible combination. Based on our experimentation on real-world dataset, we demonstrate our model’s effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern.
Author(s): Baqir A, Ali M, Jaffar S, Sherazi HHR, Lee M, Bashir AK, Al Dabel MM
Publication type: Article
Publication status: Published
Journal: Scientific Reports
Year: 2024
Volume: 14
Online publication date: 14/08/2024
Acceptance date: 07/08/2024
Date deposited: 28/08/2024
ISSN (electronic): 2045-2322
Publisher: Springer Nature
URL: https://doi.org/10.1038/s41598-024-69687-8
DOI: 10.1038/s41598-024-69687-8
Data Access Statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
PubMed id: 39143145
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