Abstract
The digital transformation of tourism has turned traveler reviews into a continuously expanding record of what visitors value, yet this evidence is fragmented across platforms and languages and is usually reduced to star ratings that obscure the underlying structure of preferences. This study develops a method for the aspect-based semantic analysis of tourist preferences and applies it to Kazakhstan, an emerging destination whose digital space spans three languages. We assembled a corpus of 571,153 reviews from 2GIS, Booking, TripAdvisor, and GetYourGuide in Russian, Kazakh, and English, balanced it to 234,663 reviews, and annotated a gold set of 1500 reviews against an 11-aspect taxonomy. Aspect categories were detected with a linear support vector model, and their sentiment was classified with a multilingual transformer, enabling reliable processing of short, code-mixed texts. The results show that platforms do not merely differ in tone but describe fundamentally different destinations. 2GIS, which carries the domestic voice, diverges sharply from the international platforms, whereas TripAdvisor and GetYourGuide are almost indistinguishable. Domestic and inbound tourists likewise emphasize different aspects, with food and service dominating domestic reviews and location, infrastructure, and nature dominating inbound ones. These findings demonstrate that any single-source analysis yields a systematically biased picture of a destination and argue for multilingual, multi-platform designs in tourism analytics.