Despite the important role of the sentiment of customer reviews in the tourist decision-making process, it has received limited attention from the field of tourism demand forecasting. This study aims to examine the sentiment information of customer reviews and explore its potential in enhancing hotel demand forecast. Empirically, four Macau luxury hotels are selected and their customer reviews are crawled from two popular online platforms. A deep learning method, the Long Short-Term Memory model, is used to extract sentiment information from consumer reviews. Three sentiment indices, namely, bullish index, average index and variance index, are constructed and examined. The effectiveness of these sentiment indices is further evaluated by the autoregressive integrated moving average with exogenous variables model. Empirical results indicate that the inclusion of the sentiment indices helps with improving forecast accuracy. The findings of this study further emphasise the importance of textual content to hotel practitioners in terms of strategy formulation, revenue management and competitiveness enhancement.
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