ISSN: 0973-7510

E-ISSN: 2581-690X

Research Article | Open Access
S.V. Praveen1 , Rosemol Boby1, Roshan Shaji1, Deepak Chandran2,Nawfal R. Hussein3, Sirwan Khalid Ahmed4, Shopnil Akash5 and Kuldeep Dhama6
1Xavier Institute of Management and Entrepreneurship Bangalore, Department of Analytics, Hosur Rd, Phase 2, Electronic City, Bengaluru, Karnataka, India.
2Department of Veterinary Sciences and Animal Husbandry, Amrita School of Agricultural Sciences, Amrita Vishwa Vidyapeetham University, Coimbatore, Tamil Nadu, India.
3Department of Biomedical Sciences, College of Medicine, University of Zakho; Kurdistan Region of Iraq, Iraq.
4Department of Pediatrics, Rania Pediatric & Maternity Teaching Hospital, Rania, Sulaymaniyah, Kurdistan Region, 46012, Iraq.
5Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh.
6Divison of Pathology, ICAR -Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India.
Article Number: 8513 | © The Author(s). 2023
J Pure Appl Microbiol. 2023;17(1):515-523. https://doi.org/10.22207/JPAM.17.1.45
Received: 18 February 2023 | Accepted: 28 February 2023 | Published online: 02 March 2023
Issue online: March 2023
Abstract

Concerns about an increase in cases during the COVID-19 pandemic have been heightened by the emergence of a new Omicron subvariant XBB.1.5 that joined the previously reported BF.7 as a source of public health concern. COVID-19 cases have been on the rise intermittently throughout the ongoing pandemic, likely because of the continuous introduction of SARS-CoV-2 subtypes. The present study analyzed the Indian citizen’s perceptions of the latest covid variants XBB.1.5 and BF.7 using the natural language processing technique, especially topic modeling and sentiment analysis. The tweets posted by Indian citizens regarding this issue were analyzed and used for this study. Government authorities, policymakers, and healthcare officials will be better able to implement the necessary policy effectively to tackle the XBB 1.5 and BF.7 crises if they are aware of the people’s sentiments and concerns about the crisis. A total of 8,54,312 tweets have been used for this study. Our sentiment analysis study has revealed that out of those 8,54,312 tweets, the highest number of tweets (n = 3,19,512 tweets (37.3%)) about COVID variants XBB.1.5 and BF.7 had neutral sentiments, 3,16,951 tweets (37.1%) showed positive sentiments and 2,17,849 tweets (25.4%) had negative sentiments. Fear of the future and concerns about the immunity of the vaccines are of prime concerns to tackle the ongoing pandemic.

Keywords

XBB 1.5, BF.7, Omicron Subvariants, Natural Language Processing, Sentiment Analysis, Topic Modeling, Twitter-based Analysis

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© The Author(s) 2023. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.