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 (Penerbit Goodwood, 2025)
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.