Innovating data-driven tourism reputation management: methodological foundations of the tourism online reputation index (TORI)

Abstract

The widespread use of social media has significantly amplified the role of online reputations in shaping the image and competitiveness of tourism destinations. This study proposes an innovative methodology that combines big data techniques with geolocated user-generated content to develop a comprehensive tourism online reputation index (TORI). The TORI aims to quantify and monitor tourists’ perceptions of destinations in a structured and scalable way. The methodology integrates the cross-industry standard process for data mining (CRISP-DM) and knowledge discovery in databases (KDD) frameworks to ensure a rigorous, systematic approach to data collection, processing, and analysis. An ontology is developed to categorize and structure the diverse attraction points within destinations, and natural language processing (NLP) techniques are employed to perform sentiment analysis and generate tourist profiles on the basis of online reviews. The proposed methodology is validated through a case study in the province of Burgos, Spain, illustrating its practical relevance for enhancing data-driven decision-making in the context of smart tourism destinations (STDs). The results are presented through an interactive scorecard that facilitates intuitive interpretation by tourism stakeholders and supports strategic planning. From a theoretical perspective, this study contributes to the literature by offering a quantitative and standardized approach to measuring online reputation, addressing the lack of integrated tools and human-centered vision in current tourism research. In practice, it provides a replicable and adaptable solution for destination managers, particularly in rural and sparsely populated areas, to improve reputation management, support sustainable development, and strengthen destination competitiveness in the digital era.