Skip to main content

Research Repository

Advanced Search

Using social media big data for tourist demand forecasting: A new machine learning analytical approach

Li, Yulei; Lin, Zhibin; Xiao, Sarah

Using social media big data for tourist demand forecasting: A new machine learning analytical approach Thumbnail


Authors

Yulei Li



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
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

Files







You might also like



Downloadable Citations