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An AI Framework for Unlocking Actionable Insights from Text Reviews: A Cultural Heritage Case Study

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  • Olga Mirković Maksimović

    (Multidisciplinary PhD Studies, University of Belgrade, 1 Studentski trg, 11000 Belgrade, Serbia)

  • Matea Lukić

    (Faculty of Organizational Sciences, University of Belgrade, 154 Jove Ilića, 11000 Belgrade, Serbia)

  • Ana Poledica

    (Faculty of Organizational Sciences, University of Belgrade, 154 Jove Ilića, 11000 Belgrade, Serbia)

  • Ilija Antović

    (Faculty of Organizational Sciences, University of Belgrade, 154 Jove Ilića, 11000 Belgrade, Serbia)

  • Dušan Savić

    (Faculty of Organizational Sciences, University of Belgrade, 154 Jove Ilića, 11000 Belgrade, Serbia)

Abstract

This paper introduces a general AI text review framework for the automated analysis of textual reviews using advanced natural language processing techniques. The framework uniquely integrates sentiment analysis, topic modeling, and abstractive summarization within a modular architecture. It leverages transformer-based models (e.g., DistilBERT and FASTopic), vector databases, and caching mechanisms to ensure scalability and real-time performance. To validate the general approach, we developed a domain-specific implementation, VisitorLens AI, which performs advanced textual analysis for Google Maps reviews of the UNESCO World Heritage Site, Kotor Fortress. We demonstrated that the designed system generates structured and actionable insights for both tourists and local authorities, and increases institutional capacity to evaluate UNESCO criteria compliance. Finally, we performed both quantitative and expert evaluations, demonstrating the high performance of our framework across NLP tasks. The outputs confirm the framework’s generalizability, robustness, and practical value across domains.

Suggested Citation

  • Olga Mirković Maksimović & Matea Lukić & Ana Poledica & Ilija Antović & Dušan Savić, 2025. "An AI Framework for Unlocking Actionable Insights from Text Reviews: A Cultural Heritage Case Study," Mathematics, MDPI, vol. 13(17), pages 1-24, August.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:17:p:2701-:d:1730301
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    References listed on IDEAS

    as
    1. Marco Rossetti & Fabio Stella & Markus Zanker, 2016. "Analyzing user reviews in tourism with topic models," Information Technology & Tourism, Springer, vol. 16(1), pages 5-21, March.
    2. Angelo Geninatti Cossatin & Noemi Mauro & Fabio Ferrero & Liliana Ardissono, 2025. "Tell me more: integrating LLMs in a cultural heritage website for advanced information exploration support," Information Technology & Tourism, Springer, vol. 27(2), pages 385-416, June.
    3. Nayoung Jeong & Jihwan Lee, 2024. "An Aspect-Based Review Analysis Using ChatGPT for the Exploration of Hotel Service Failures," Sustainability, MDPI, vol. 16(4), pages 1-25, February.
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