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.
