Utilizing textual data from online reviews for daily tourism demand forecasting: A deep learning approach leveraging word embedding techniques

Abstract

Accurately estimating daily tourism volumes is crucial for optimizing operational strategies and enhancing visitor experiences at tourist destinations. In this study, we leverage historical tourism volume data, search engine data, and online review data (including textual review contents) to forecast daily tourism demand. We develop a deep learning framework that includes a feature selection module to select search engine indices, a word embedding module to transform review texts into numerical predictors, and an extreme learning machine (ELM) enhanced with the whale optimization algorithm (WOA) for predictive modeling. Based on different word embedding techniques, we investigated two specific forecasting methods: one based on term frequency-inverse document frequency (TF-IDF) and Transformer, and the other based on the standard pre-trained bidirectional encoder representations from Transformers (BERT). These two methods enhance predictive accuracy while ensuring fast training and inference speeds, making it suitable for the high-frequency daily tourism forecasting task. Experimental results demonstrate that the two methods significantly outperform traditional approaches that rely solely on numerical data sources, achieving lower prediction errors and faster inference speeds compared to established benchmark methods, highlighting their potential to contribute to advancements in tourism prediction methodologies.