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.

中文翻译：

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话题和情绪重要吗？在线评论对酒店需求预测的预测能力


整合文本数据进行预测的研究主要集中在文本中反映的整体情绪。然而，文本数据传达了各种类型的信息，例如评论主题，这在预测酒店需求时可能很有用。本研究旨在结合主题建模和情感分析来提高酒店需求的预测性能。具体来说，采用潜在狄利克雷分配（LDA）主题建模技术和长短期记忆（LSTM）模型来构建基于主题的情感指数。采用带有解释变量类型模型的自回归积分移动平均（ARIMA）和混合数据采样（MIDAS）模型来评估预测能力。结果表明，使用主题情绪和 COVID-19 变量进行 MIDAS 预测可以生成最准确的预测。研究结果将在线文本大数据在酒店需求预测研究中的应用联系起来。酒店管理层可以利用这些在线数据进行短期预测，以促进人群管理并更有效地应对不可预见的公共卫生事件。