Guest Sentiment Analysis Based on Online Reviews to Optimize Guest Satisfaction at Hotel X - I Dewa Gde Tri Jaya Cahyadi; Putu Diah Sastri Pitanatri; Prastha Adyatma (Brilliant International Journal Of Management And Tourism, 2025)
Tourism has become one of the largest and fastest-growing industries globally. Advances in technology and communication have brought significant changes in various aspects, especially in the hospitality industry. Using a dataset from November 2024 to January 2025, this sentiment analysis was conducted using the Naive Bayes classification method (Gaussian, Multinomial, and Bernoulli). The results show that 72.99% of reviews are positive, while 19.08% are negative and 7.93% are neutral. The Naive Bayes model demonstrates high accuracy in classifying positive sentiment but exhibits differences in classification accuracy for the negative and neutral categories due to class imbalance. Occupancy data reveals a peak in 2023 and a significant decline in 2024. This study reveals the importance of ongoing sentiment analysis to establish management strategies, address service gaps, and improve guest satisfaction, which aims to improve guest satisfaction in the competitive hospitality market.