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

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.