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This Author published in this journals
All Journal Journal of Sustainable Tourism and Entrepreneurship Studi Ilmu Manajemen dan Organisasi
Ni Wayan Chintia Pinaria
Politeknik Pariwisata Bali, Bali, Indonesia
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Analisis Sentimen Ulasan Tamu Untuk Meningkatkan Hunian Kamar Boutique Hotel di Kuta Dewa Ayu Kirana Maya Candra; Putu Diah Sastri Pitanatri; Ni Wayan Chintia Pinaria
Studi Ilmu Manajemen dan Organisasi Vol 6 No 2 (2025): Juli
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/simo.v6i2.4701

Abstract

Purpose: This study aims to analyze guest review sentiments of Fourteen Roses Boutique Hotel Kuta on the Booking.com platform, to identify aspects of hotel services that require improvement and can inform strategies to increase room occupancy rates. Methodology/approach: A quantitative approach is used, with data collected via web scraping from Booking.com. The review texts undergo preprocessing for cleaning and structuring, followed by sentiment classification using the Naïve Bayes algorithm and TF-IDF for feature extraction. Python is used for analysis, and the results are visualized using word clouds and sentiment distribution charts. Results/findings: The analysis reveals that 49.0% of the reviews express positive sentiment, highlighting appreciation for staff service, room comfort, facilities, and hotel location. Meanwhile, 21.0% show negative sentiment, mainly concerning breakfast quality, noise at night, and bathroom conditions. Additionally, 14.3% of the reviews are neutral, often using terms like “standard,” “ok,” or “normal,” indicating weakly held opinions. Despite data imbalance, the Naïve Bayes model achieved an accuracy of 78%. Conclutions: Overall guest perceptions are positive, but negative and neutral feedback still requires attention. The sentiment analysis results and word cloud visualizations serve as useful references for identifying areas needing service improvements to enhance occupancy rates. Limitations: The study focuses solely on Booking.com data. Future research should incorporate multiple platforms and explore more advanced classification techniques to better handle data imbalance. Contribution: This study provides insights into guest sentiment that can help hotel management design more targeted strategies to remain competitive in the hospitality industry.
Analisis Sentimen dalam Mengurangi Pembatalan Reservasi di The Westin Resort & Spa Ubud Ni Kadek Indah Puspaningrum; Putu Diah Sastri Pitanatri; Ni Wayan Chintia Pinaria
Studi Ilmu Manajemen dan Organisasi Vol 6 No 2 (2025): Juli
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/simo.v6i2.4707

Abstract

Purpose: This study aimed to analyze guest sentiment from reviews on Booking.com to identify insights that may help reduce room reservation cancellations. Methodology/approach: A quantitative approach was used, with data collected through web scraping using Python from customer reviews on the Booking. com website. A total of 433 reviews were analyzed using the Naïve Bayes classification method for sentiment analysis. Results/findings: The analysis revealed that 362 reviews (83.6%) contained positive sentiments, indicating high guest satisfaction, particularly with staff service, room quality, and facilities such as the pool and breakfast. Meanwhile, 71 reviews (16.4%) expressed negative sentiments, mainly focusing on room quality and overall hotel experience. The Naïve Bayes model achieved a classification accuracy of 91%, with a high F1-score of 95% for positive sentiments but only 31% for negative sentiments, highlighting data imbalance. Based on these findings, hotel management is advised to pay more attention to key aspects such as “staff,” “room,” “pool,” and “breakfast” to enhance guest satisfaction and minimize reservation cancellations. Conclusion: Most reviews reflected positive sentiments, indicating a high level of satisfaction. However, negative reviews, although fewer, must be further evaluated to improve service quality, especially given the classification model’s lower performance on negative sentiments. Limitations: This study is limited to Booking.com reviews for The Westin Resort & Spa Ubud, based on 433 entries. Contribution: This study provides a sentiment analysis approach to help hotel management better understand customer feedback and develop strategies to reduce cancellation rates.
Sentiment Analysis of Airbnb Guest Reviews in Increasing Reservations at Tri Datu Properties Cokorda Zaic Arizena; Putu Diah Sastri Pitanatri; Ni Wayan Chintia Pinaria
Journal of Sustainable Tourism and Entrepreneurship Vol. 7 No. 4 (2026): June
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/joste.v7i4.4230

Abstract

Purpose: Few studies have applied sentiment analysis to identify service improvement priorities from Airbnb guest reviews. Therefore, this study aims to analyze guest review sentiments to generate insights for improving service quality and increasing reservations. Methodology: A quantitative computational approach was applied to 599 Airbnb guest reviews collected via API scraping. Reviews were processed in Python, transformed using term frequency-inverse TF-IDF, and classified using the Support Vector Machine (SVM) algorithm. A word cloud was used to visualize the key review topics. Results: The proposed SVM model achieved an accuracy of 98.33 %, demonstrating a robust sentiment classification performance. Most reviews (94.4%) expressed positive sentiments, highlighting property quality, host hospitality, and stay experience, whereas negative reviews (5.5%) primarily concerned cleanliness and facility maintenance. These findings identify key service attributes for improving guest satisfaction and supporting future reservations in the hospitality industry. Conclusions: Guest perceptions are largely positive, indicating high satisfaction, although negative reviews remain important to be addressed. These findings demonstrate that sentiment analysis can identify service improvement priorities to enhance service quality and increase reservation numbers. Limitations: This study focused on analyzing guest sentiment toward Tri Datu Properties on the Airbnb platform with 599 data entries. Contributions: This study extends the Expectation Confirmation Theory by integrating sentiment analysis and machine learning to transform large-scale Airbnb reviews into actionable insights for hospitality decision-making.
Co-Authors Cokorda Zaic Arizena Dewa Ayu Kirana Maya Candra Ni Kadek Indah Puspaningrum Putu Diah Sastri Pitanatri