Unveiling public perceptions at the beginning of lockdown: an application of structural topic modeling and sentiment analysis in the UK and India

Kang, X. and Stamolampros, P. orcid.org/0000-0001-8143-7244 (2024) Unveiling public perceptions at the beginning of lockdown: an application of structural topic modeling and sentiment analysis in the UK and India. BMC Public Health, 24. 2832. ISSN 1471-2458

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Item Type: Article
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© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords: COVID-19, Lockdown, Social media, Lexicon-based sentiment analysis, Structural topic modeling
Dates:
  • Published: 15 October 2024
  • Published (online): 15 October 2024
  • Accepted: 23 September 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Business (Leeds) > Analytics, Technology & Ops Department
Depositing User: Symplectic Publications
Date Deposited: 09 Oct 2024 15:04
Last Modified: 18 Oct 2024 16:14
Published Version: https://bmcpublichealth.biomedcentral.com/articles...
Status: Published
Publisher: BMC
Identification Number: 10.1186/s12889-024-20160-1
Open Archives Initiative ID (OAI ID):

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