Natural language processing study reveals language-specific negative feedback patterns in bilingual hotel reviews, highlighting the importance of culturally sensitive sentiment analysis.
Online customer reviews are a critical source of insight for evaluating hotel service quality in the current digital era. This study analyzes over 150,000 English and Vietnamese reviews from TripAdvisor, Booking.com, and Agoda, using a multilingual NLP framework. DistilBERT and PhoBERT were employed for sentiment classification, while BERTopic extracted latent themes. The datasets show a strong positivity bias. A combined RandomOverSampler and K-fold strategy was used to address class imbalance and improve sentiment prediction. The results indicate that BERTopic outperforms LDA in coherence and interpretability, producing fine-grained topics related to staff service, cleanliness, amenities, food quality, and value for money. Cross-language comparisons reveal distinct patterns in how English and Vietnamese guests articulate negative sentiment, emphasizing the need for culturally sensitive analysis. The study provides a scalable method for multilingual hotel review analytics and offers actionable insights to further enhance service in Vietnam's hospitality industry.
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Le et al. (2026) studied this question.
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