Do topic and sentiment matter? Predictive power of online reviews for hotel demand forecasting

• Published in: International journal of hospitality management Vol. 120; p. 103750
• Main Authors: Wu, Doris Chenguang, Zhong, Shiteng, Song, Haiyan, Wu, Ji
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2024
• Subjects: Hotel demand forecasting; LDA topic modeling; MIDAS model; Online review text; Sentiment analysis; Hotel demand forecasting; Sentiment analysis; LDA topic modeling; MIDAS model; Online review text
• ISSN: 0278-4319; 1873-4693
• DOI: 10.1016/j.ijhm.2024.103750

Studies integrating textual data for forecasting have mainly focused on the overall sentiment reflected in text. Yet textual data convey various types of information, such as review topics, that can be beneficial when forecasting hotel demand. This study aims to combine topic modeling and sentiment analysis to improve forecasting performance of hotel demand. Specifically, the latent Dirichlet allocation (LDA) topic modeling technique and the long short-term memory (LSTM) model are employed to construct topic-based sentiment indices. The autoregressive integrated moving average (ARIMA) with explanatory variable–type models and mixed data sampling (MIDAS) models are adopted for the evaluation of predictive power. Results reveal that MIDAS forecasting with topic–sentiment and COVID-19 variables generates most accurate forecasts. The findings contextualize the application of online textual big data in hotel demand forecasting research. Hotel management can utilize these online data for short-term forecasting to facilitate crowd management and respond more effectively to unforeseen public health events. •Hotel forecasting accuracy is improved using online textural big data.•Topic modeling and sentiment analysis are combined for textual mining.•Principal component analysis is used for COVID-19 variable construction.•MIDAS forecasting model generates most accurate forecasts.