Yulei Li
Using social media big data for tourist demand forecasting: A new machine learning analytical approach
Li, Yulei; Lin, Zhibin; Xiao, Sarah
Authors
Professor Zhibin Lin zhibin.lin@durham.ac.uk
Professor
Professor Sarah Xiao hong.xiao@durham.ac.uk
Head Of Dept - Management & Marketing
Abstract
This study explores the possibility of using a machine learning approach to analysing social media big data for tourism demand forecasting. We demonstrate how to extract the main topics discussed on Twitter and calculate the mean sentiment score for each topic as the proxy of the general attitudes towards those topics, which are then used for predicting tourist arrivals. We choose Sydney, Australia as the case for testing the performance and validity of our proposed forecasting framework. The study reveals key topics discussed in social media that can be used to predict tourist arrivals in Sydney. The study has both theoretical implications for tourist behavioural research and practical implications for destination marketing.
Citation
Li, Y., Lin, Z., & Xiao, S. (2022). Using social media big data for tourist demand forecasting: A new machine learning analytical approach. Journal of Digital Economy, 1(1), 32-43. https://doi.org/10.1016/j.jdec.2022.08.006
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 16, 2022 |
Online Publication Date | Aug 27, 2022 |
Publication Date | 2022-06 |
Deposit Date | Aug 25, 2022 |
Publicly Available Date | Sep 28, 2022 |
Journal | Journal of Digital Economy |
Electronic ISSN | 2773-0670 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 1 |
Issue | 1 |
Pages | 32-43 |
DOI | https://doi.org/10.1016/j.jdec.2022.08.006 |
Public URL | https://durham-repository.worktribe.com/output/1193202 |
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 2022 The Authors. Published by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC
BY license (http://creativecommons.org/licenses/by/4.0/).
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