Examining post COVID-19 tourist concerns using sentiment analysis and topic modeling
Balasubramanian, Sreejith 
ORCID: https://orcid.org/0000-0002-0475-7305, Kaitheri, Supriya, Nanath, Krishnadas ORCID: https://orcid.org/0000-0002-3515-9084, Sreejith, Sony and Paris, Cody Morris ORCID: https://orcid.org/0000-0002-0339-2471
(2021)
Examining post COVID-19 tourist concerns using sentiment analysis and topic modeling.
Wörndl, Wolfgang, Koo, Chulmo and Stienmetz, Jason L., eds.
Information and Communication Technologies in Tourism 2021: Proceedings of the ENTER 2021 eTourism Conference, January 19–22, 2021.
In: ENTER 2021 eTourism Conference, 19-21 Jan 2021, Virtual.
pbk-ISBN 9783030657840, e-ISBN 9783030657857.
[Conference or Workshop Item]
(doi:10.1007/978-3-030-65785-7_54)
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Abstract
The COVID-19 pandemic has had a destructive effect on the tourism sector, especially on tourists’ fears and risk perceptions, and is likely to have a lasting impact on their intention to travel. Governments and businesses worldwide looking to revive and revamp their tourism sector, therefore, must first develop a critical understanding of tourist concerns starting from the dreaming/planning phase to booking, travel, stay, and experiencing. This formed the motivation of this study, which empirically examines the tourist sentiments and concerns across the tourism supply chain. Natural Language Processing (NLP) using sentiment analysis and Latent Dirichlet Allocation (LDA) approach was applied to analyze the semi-structured survey data collected from 72 respondents. Practitioners and policymakers could use the study findings to enable various support mechanisms for restoring tourist confidence and help them adjust to the ’new normal.’
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords (uncontrolled): | Tourism supply chain, Emotions, Lexicon-based approach |
| Research Areas: | A. > Business School > Marketing, Branding and Tourism |
| Item ID: | 32271 |
| Notes on copyright: | © The Author(s) 2021. Open Access.
This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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 license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license 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. |
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| Depositing User: | Cody Paris |
| Date Deposited: | 09 Apr 2021 08:59 |
| Last Modified: | 24 Sep 2021 16:15 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/32271 |
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