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Article | Green Intelligent Systems and Applications | Volume 5 - Issue 1 - 2025 | 51-60 | https://doi.org/10.53623/gisa.v5i1.588
© 2025 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Twitter Sentiment Analysis of Mental Health Issues Post COVID-19

Author(s): Panca Dewi Pamungkasari 1 ORCID https://orcid.org/0000-0001-6471-2398 , Sari Ningsih 1 , Achmad Pratama Rifai 2 ORCID https://orcid.org/0000-0003-4890-8344 , Alisyafira Sayyidina Nandila 1 , Huu Tho Nguyen 3 ORCID https://orcid.org/0000-0001-8558-5906 , Sathish Kumar Penchala 4 ORCID https://orcid.org/0000-0002-1129-6789
Author(s) information:
1 Faculty of Communication and Information Technology Universitas Nasional, Jakarta, Indonesia
2 Department of Mechanical and Industrial Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
3 Faculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
4 Indore Institute of Science and Technology, Indore, India

Corresponding author

Green Intelligent Systems and Applications

Volume 5 - Issue 1 - 2025

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The Coronavirus Disease 2019 (COVID-19) impacted many aspects of daily life, including mental health, as some individuals struggled to adjust to the rapid changes brought on by the pandemic. This paper investigated sentiment analysis of Twitter data following the COVID-19 pandemic. Specifically, we analyzed a large corpus of tweets to understand public sentiment and its implications for mental health in the post-pandemic context. The Naïve Bayes and Support Vector Machine (SVM) classifiers were used to categorize tweets into positive, negative, and neutral sentiments. The collected tweet data samples showed that 38.35% were neutral, 32.56% were positive, and 29.09% were negative. Results using the SVM method showed an accuracy of 84%, while Naïve Bayes achieved 80% accuracy.

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SUBMITTED: 16 January 2025
ACCEPTED: 24 March 2025
PUBLISHED: 29 March 2025
SUBMITTED to ACCEPTED: 67 days
DOI: https://doi.org/10.53623/gisa.v5i1.588

Cite this article
Pamungkasari, P. D. ., Ningsih, S. ., Rifai, A. P., Nandila, A. S. ., Nguyen, H. T., & Penchala, S. K. . (2025). Twitter Sentiment Analysis of Mental Health Issues Post COVID-19. Green Intelligent Systems and Applications, 5(1), 51–60. https://doi.org/10.53623/gisa.v5i1.588
Keywords
Mental Health COVID-19 Sentiment Analysis Twitter Na¨ıve Bayes Support Vector Machine
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