<![CDATA[Purpose: Competition in Bali’s hospitality industry is intensifying due to the growing number of star-rated hotels and available room capacity. This trend is driven by globalization, digital advancements, and changing consumer demands for service quality. This study aims to analyze domestic tourists’ preferences through sentiment analysis of online reviews to help optimize room occupancy rates. Methodology/approach: Using a text mining approach with the Naïve Bayes algorithm, this study analyzes 429 Tripadvisor reviews of The St. Regis Bali Resort. Data was collected via web scraping using Python, covering reviews from the past five years to reflect current guest preferences. Results/findings: The results show that 80.19% of the reviews express positive sentiment, indicating high satisfaction with service quality, staff professionalism, room comfort, and a strong brand image. The Naïve Bayes classifier achieved an accuracy of 83.72%, performing well in identifying positive sentiment, though less effective for neutral and negative reviews due to class imbalance. Conclusion: Sentiment analysis using Naïve Bayes effectively captures positive guest sentiment, though further refinement is needed for neutral and negative classifications. These insights support more precise service improvements and marketing strategies to boost loyalty and occupancy. Limitations: This study is limited to Tripadvisor reviews of a single luxury hotel in Bali, which may affect the generalizability of the findings. Contribution: The study highlights the strategic value of guest reviews in informing hotel decision-making, helping to tailor services and promotions to meet domestic tourists’ preferences more effectively.]]>
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