A thematic investigation of tourist experiential gaps in Moroccan cultural heritage museums using a sentiment-guided BERTopic text mining approach

This study investigates tourist experiential gaps in Moroccan cultural heritage museums through a problem-oriented lens, treating negative tourist feedback as a diagnostic tool for institutional and policy improvement. Drawing on TripAdvisor reviews from 37 museums, the analysis combines polarity-based sentiment segmentation using TextBlob with the BERTopic algorithm. The resulting topics are mapped through thematic cartography to evaluate their centrality and density, producing a structured typology of museum-related issues. The findings underscore a range of persistent issues, from the lack of English translations and high service costs to poorly designed exhibitions and internal disorganization. Additional concerns include unannounced closures, limited physical space in small museums, and the minimal presence of fashion-related displays. These issues reflect value gaps, with functional gaps strongly outweighing economic and epistemic ones. In response, the study proposes measures including multilingual access, digital tools, improved exhibit design, and fair pricing. Grounded in negative feedback, it presents a reverse reading of museum value creation, shifting attention to unmet expectations. The PERVAL model is extended to capture unperceived value, and a transferable diagnostic approach is introduced. The study provides museum managers with a clear blueprint to better align the tourist experience with institutional goals in reputation-sensitive settings.