Boosting a Smart Tourism Destination with Big Data techniques, sentiment analysis and geo-located information: a case study from inland Spain

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Boosting a Smart Tourism Destination with Big Data techniques, sentiment analysis and geo-located information: a case study from inland Spain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Boosting a Smart Tourism Destination with Big Data techniques, sentiment analysis and geo-located information: a case study from inland Spain Julio César Puche-Regaliza, Isabel Marcilla-Lombraña, Paula Antón-Maraña, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6853446/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Information Technology & Tourism → Version 1 posted 9 You are reading this latest preprint version Abstract The proliferation of social media has amplified the impact of online reputation on tourism destinations. This study proposes an innovative methodology based on Big Data techniques and geo-located information to develop a synthetic tourism online reputation index (TORI) to assess tourists’ perceptions of destinations. The methodology integrates the Cross-Industry Standard Process for Data Mining (CRISP-DM) and Knowledge Discovery in Databases (KDD) frameworks, ensuring a rigorous and efficient analysis of tourism sector data. An ontology is defined to categorize attraction points of tourist destinations, and Natural Language Processing (NLP) techniques are applied to conduct sentiment analyses of tourist reviews and analyze the tourist profile. The methodology is validated through a case study of the province of Burgos, Spain, demonstrating its practical application in strengthening the technological axis of the Smart Tourism Destination (STD) model and its potential to support sustainable tourism development in rural and sparsely populated areas. The results are presented in an interactive scorecard, facilitating data interpretation and strategic decision-making by tourism stakeholders. Overall, the study contributes to the literature by addressing key gaps, such as the lack of quantitative and standardized approaches to online reputation measurement, the need for a comprehensive index that encompasses all attraction points within a destination and the importance of offering a human-centered vision, focusing on tourist perceptions and experiences. In conclusion, the proposed methodology offers a data-driven approach for understanding and improving destination reputation, which is crucial for maintaining competitiveness and ensuring sustainable tourism development in the digital age. Smart Tourism Destinations Tourism Intelligence System Inland Rural Tourism Online Reputation Tourism Index Tourism Decision Making Sustainable Tourism Development Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Information Technology & Tourism → Version 1 posted Editorial decision: Revision requested 29 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers invited by journal 13 Jun, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 09 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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