Harnessing Generative AI for Comprehensive Analysis of Tourist Reviews

Julia Marti-Ochoa (), Eva Martin-Fuentes () and Juan Pedro Mellinas ()
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Julia Marti-Ochoa: Universitat de Lleida
Eva Martin-Fuentes: Universitat de Lleida
Juan Pedro Mellinas: University of Murcia

Chapter 3 in Information and Communication Technologies in Tourism 2026, 2026, pp 31-43 from Springer

Abstract: Abstract This study introduces a scalable and replicable framework based on Agentic Process Automation (APA) to analyse verified customer reviews in the tourism and hospitality sector. The proposed system integrates workflow automation, data scraping and generative artificial intelligence to process large volumes of user-generated content from Trustpilot’s Activities and Tours subcategory. Unlike traditional approaches, this framework enables extracting multidimensional insights from unstructured textual data including sentiment, emotional tone, language, customer typology, tourist profile, and travel party composition. Grounded in a comprehensive review of the prior literature, the selected analytical dimensions reflect key variables previously identified as being relevant for understanding post-consumption behaviour in tourism. The framework leverages generative AI agents to perform multilingual and context-aware content analysis, transforming qualitative feedback into structured marketing intelligence. This approach not only enhances the granularity of customer understanding but also supports data-driven decision-making for tourism businesses seeking to optimise loyalty strategies, service design, and segmentation efforts. Overall, the study demonstrates the potential of AI-powered review mining as a strategic tool for capturing authentic customer experiences in a verified and domain-specific environment.

Keywords: Agentic Process Automation (APA); Travel activities and tours; Artificial intelligence (AI) (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-23925-9_3

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DOI: 10.1007/978-3-032-23925-9_3

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