Unravelling long-stay tourist experiences and satisfaction: text mining and deep learning approaches

Abstract

ABSTRACT Despite the growing interest in long-stay tourism, a comprehensive understanding of long-stay tourists’ experiences remains largely unexplored. This study aims to explore the dimensions of experiences among long-stay tourists and examine their impact on overall satisfaction. By leveraging online reviews from long-stay tourists and employing a machine learning-based text mining approach, including topic modelling and sentiment analysis, we identify specific tourist experiences and evaluate their emotional responses. An econometric analysis is then conducted to assess the relationship between these experience dimensions and satisfaction. Our findings reveal 10 experience dimensions of long-stay tourists, which are interpreted through the experiencescape model. Notably, except for the attraction dimension, all identified dimensions significantly influence long-stay tourists’ satisfaction This study not only contributes to the existing literature by comprehensively identifying the experience dimensions that affect satisfaction but also offers valuable insights for stakeholders by providing guidance on how to enhance long-stay tourism in destinations.