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Vol. 7 No. 4 (2026): June

Sentiment Analysis of TripAdvisor Guest Reviews to Enhance Luxury Hotel Performance

https://doi.org/10.35912/joste.v7i4.4276
30 Jun 2026

Abstract

Purpose: This study aims to analyze guest sentiment expressed in TripAdvisor reviews of Conrad Bali using the Multinomial Naïve Bayes algorithm to identify service attributes discussed by guests and generate evidence-based managerial insights that support hotel performance enhancement.

Methodology: A quantitative text-mining approach was used. A total of 833 TripAdvisor reviews (2023–2025) were collected using the Apify web scraping tool.. The analysis involved text preprocessing, TF-IDF feature extraction, sentiment classification using the Multinomial Naïve Bayes algorithm, Synthetic Minority Oversampling Technique (SMOTE) for class balancing, confusion matrix evaluation, and word cloud visualization using Python.

Results: The findings revealed that Positive, neutral, and negative sentiments accounted for 88.48%,8.64%and 2.88%, respectively. The application of SMOTE improved the model classification balance, increasing the F1-score from 0.8298 to 0.8596. Words related to staff, service, room, and hotel facilities appeared most frequently within positively classified reviews, indicating recurring discussion themes associated with favorable guest experiences.

Conclusions: The findings demonstrate that sentiment analysis can effectively identify guest perceptions and recurring service attributes discussed in online hotel reviews. These insights support evidence-based service evaluation and managerial decision-making, thereby contributing to continuous service improvement and hotel performance enhancement.

Limitations: The study was limited to TripAdvisor reviews, and the word clouds reflected word frequency rather than contextual meaning. In addition, the relatively small number of negative reviews may limit the classifier's ability to generalize minority sentiment patterns.

Contributions: This study demonstrates how online guest reviews can be transformed into evidence-based managerial insights for service improvement, evaluation, and hotel performance enhancement.

Keywords

Hotel Performance Naive Bayes Classification Sentiment Analysis TripAdvisor Reviews Avis Hôteliers Analyse de Sentiment Basée sur les Aspects qualité du service performance de l'hôtel classification bayésienne naïve Berbasis Aspek Analisis Sentimen Ulasan Hotel Kualitas Layanan Kinerja hotel Klasifikasi Naive Bayes

How to Cite

Melianty, S. A., Pitanatri, P. D. S., & Sulistyawati, N. L. K. S. (2026). Sentiment Analysis of TripAdvisor Guest Reviews to Enhance Luxury Hotel Performance. Journal of Sustainable Tourism and Entrepreneurship, 7(4), 441–461. https://doi.org/10.35912/joste.v7i4.4276

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Authors

1 Sri Aida Melianty
Politeknik Pariwisata Bali, Badung, Indonesia
2 Putu Diah Sastri Pitanatri
Politeknik Pariwisata Bali, Badung, Indonesia
3 Ni Luh Ketut Sri Sulistyawati
Politeknik Pariwisata Bali, Badung, Indonesia

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Vol. 7 No. 4 (2026): June
Section: Articles
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