Tourism intelligence and online emotions: Sentiment analysis in boutique hotels of Puebla
DOI:
https://doi.org/10.61273/neyart.v3i3.148Keywords:
Tourist intelligence, Sentiment Analysis, Opinion Mining, Boutique Hotels, Web User ExperiencesAbstract
In a context of digitalized and highly competitive tourism, Tourism Intelligence (TI) leverages big data from online reviews to improve strategic decision-making and promote sustainability in the hotel sector. This study quantitatively analyzes tourist satisfaction in 20 boutique hotels in Puebla (Mexico), characterized by their unique design and deep cultural roots, through the examination of the emotional dimensions of the digital experience (web UX). Using a non-experimental, cross-sectional, and correlational quantitative design, 1,464 reviews collected between June and July 2025 from TripAdvisor, Booking.com, and Google Reviews were processed. Through natural language processing (NLP) techniques based on transformer models (RoBERTuito and BETO), predominant sentiments and emotions were classified.
The results reveal a clear predominance of positive sentiments (69.7%; n=1,021), compared to negative (16.3%) and neutral (14.0%) ones. The most frequent emotion was joy (52.4%), while disgust and anger together accounted for 18.2%. The average review length was only 16.3 words. Significant correlations were identified between dissatisfaction and aspects such as accessibility and service quality.
The study validates the use of sentiment analysis within Tourism Intelligence to generate predictive insights, confirm predominantly positive perceptions, and strengthen emotional bonds with boutique hotels. It is recommended to implement real-time monitoring with ReviewPro and longitudinal studies to reduce seasonal biases, thus contributing to a more responsible, competitive, and sustainable tourism model in Mexico.
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Copyright (c) 2025 Vidal Armas Torres , Ramón Sebastián Acle Mena , Franco Rojas López , Salin Armas Torres , Wendy Berenice López Torres
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