Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling

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 f...

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Autores principales: Balasubramanian, Sreejith, Kaitheri, Supriya, Nanath, Krishnadas, Sreejith, Sony, Paris, Cody Morris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7798058/
http://dx.doi.org/10.1007/978-3-030-65785-7_54
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author Balasubramanian, Sreejith
Kaitheri, Supriya
Nanath, Krishnadas
Sreejith, Sony
Paris, Cody Morris
author_facet Balasubramanian, Sreejith
Kaitheri, Supriya
Nanath, Krishnadas
Sreejith, Sony
Paris, Cody Morris
author_sort Balasubramanian, Sreejith
collection PubMed
description 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.’
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spelling pubmed-77980582021-01-11 Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling Balasubramanian, Sreejith Kaitheri, Supriya Nanath, Krishnadas Sreejith, Sony Paris, Cody Morris Information and Communication Technologies in Tourism 2021 Article 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.’ 2020-11-28 /pmc/articles/PMC7798058/ http://dx.doi.org/10.1007/978-3-030-65785-7_54 Text en © 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.
spellingShingle Article
Balasubramanian, Sreejith
Kaitheri, Supriya
Nanath, Krishnadas
Sreejith, Sony
Paris, Cody Morris
Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling
title Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling
title_full Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling
title_fullStr Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling
title_full_unstemmed Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling
title_short Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling
title_sort examining post covid-19 tourist concerns using sentiment analysis and topic modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7798058/
http://dx.doi.org/10.1007/978-3-030-65785-7_54
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