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In the last few decades, rapid advancements in digital technologies and the widespread adoption of web-based platforms have significantly transformed how individuals plan, experience, and personalize travel. Increased access to high-speed internet, smartphones, and mobile applications has driven a paradigm shift in the tourism industry from traditional travel agencies to more innovative, interactive, and user-centered platforms (Shao et al., 2019; Xiao et al., 2025). The emergence of online travel agencies, location-aware services, and vast amounts of user-generated content, including reviews, star ratings, travel blogs, and social media posts, has enabled travelers to make more informed and personalized decisions. These digital footprints not only capture the experiences of millions of users but also provide valuable insights into tourist behavior, preferences, satisfaction levels, and emotional responses to destinations and services (Shao et al., 2019; Xiao et al., 2025). Modern recommendation systems (Chen et al., 2024) attempt to leverage these rich datasets to provide personalized tourism suggestions. However, despite substantial advancements, existing approaches often fail to fully address the diverse, dynamic, and emotion-driven nature of human travel decisions.
Most tourism recommendation systems are designed around aggregated numerical ratings, historical travel patterns, or basic user profiles (Stefanovič & Ramanauskaitė, 2023). These systems typically rank destinations and hotels based on popularity, average ratings, or proximity to nearby attractions. Although such approaches provide a basic level of personalization, the resulting recommendations are often generic, static, and lack contextual awareness. In most cases, these models fail to consider the emotional and psychological factors that significantly influence travelers’ preferences at a given moment. For example, a user seeking a relaxing getaway after a stressful work period may receive recommendations similar to those provided to another user searching for adventure or nightlife simply because both previously rated a beach resort highly. This “one-size-fits-all” approach often results in unsatisfactory experiences because it overlooks the user’s current mood, travel purpose, and evolving interests.
Despite the rapid growth of intelligent recommendation systems in the tourism industry, several critical limitations remain unresolved. Most existing models are static and reactive, relying heavily on fixed data such as user demographics and historical travel behavior. As a result, they often lack the ability to adapt dynamically to a traveler’s real-time emotional state or situational context. In addition, traditional recommendation engines frequently rely on basic keyword-matching techniques (Bhende et al., 2024; Chen et al., 2024) to interpret user-generated content, which is often insufficient for accurately identifying sentiment or emotional tone. Consequently, these systems are prone to misinterpretations, particularly when dealing with sarcasm, mixed emotions, or subtle linguistic expressions. For instance, a sarcastic review such as “Great hotel—if you enjoy moldy bathrooms and rude staff!” may be incorrectly classified as positive because of the presence of the word “great,” ultimately leading to inaccurate recommendations.
Recent advancements in natural language processing (NLP) and deep learning (Bhende et al., 2024; Ghosh et al., 2025; Sivadevuni & J, 2025) have significantly refined sentiment analysis capabilities, enabling more accurate interpretation of user emotions and feedback. These methods can extract nuanced insights from textual data, thereby providing a more sophisticated foundation for recommendation systems. Despite these developments, only a limited number of systems holistically incorporate mood-based sentiment into a unified recommendation framework that integrates destination selection, hotel booking, and itinerary planning. A comprehensive solution that considers both emotional and contextual factors (Nakata et al., 2025) could substantially enhance the quality and personalization of travel experiences, thereby addressing a critical gap in existing tourism technologies.