Identifying the role of media discourse in tourism demand forecasting

ABSTRACT

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.

KEYWORDS:

• Tourism demand forecasting
• media discourse
• machine learning
• sentiment classification
• topic modelling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China: [grant no 72162014]; Hainan Provincial Natural Science Foundation of China: [grant no 2019RC060]; the specific research fund of The Innovation Platform for Academicians of Hainan Province: [grant no YSPTZX202035].