Identifying the role of media discourse in tourism demand forecasting

被引：5
|
作者：
Chen, Yihong [1 ]
Hu, Tao [1 ]
Song, Peiying [1 ]
机构：
[1] Hainan Univ, Tourism management, Haikou, Peoples R China
来源：
CURRENT ISSUES IN TOURISM | 2024年 / 27卷 / 03期
基金：
海南省自然科学基金; 中国国家自然科学基金;
关键词：
Tourism demand forecasting; media discourse; machine learning; sentiment classification; topic modelling; SOCIAL MEDIA; TIME-SERIES; CLIMATE; IMPACT; COMBINATION; ARRIVALS; GROWTH;
D O I：
10.1080/13683500.2023.2165050
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
High-frequency and timely tourism demand forecasting of fine-grained attractions is an effective tool that helps tourism stakeholders make decisions and formulate strategies. However, there are limitations due to the external sensitivity of tourism demand. This research integrated news coverage with other psychosocial variables to comprehensively explore the impact of social unrest on tourism demand. Topic modelling was applied to identify tourism news topics and potential meanings. Sentiment classification was used to convert news into structured data. Sentiment indices and quantities constituted a composite news term. The empirical results showed that the comprehensive inclusion of external elements, especially news coverage, can significantly improve the prediction performance. K-Nearest Neighbour with the news term yielded the best results.
引用
收藏
页码：413 / 427
页数：15
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