<![CDATA[Background: Tourist reviews provide information for evaluating visitor experiences and tourism village development. Objective: This study analyzes tourist sentiment toward tourism villages in Lampung Province using Google Maps reviews and Naive Bayes classification. Methods: A total of 2,847 reviews were collected from nine developed tourism villages registered in the Tourism Village Network. Data were preprocessed, transformed into textual features, and classified using Naive Bayes with 80% training and 20% testing data. Results: Positive sentiment accounted for 73.38% of reviews, neutral 17.98%, and negative 8.64%. The Naive Bayes model achieved 81.56% accuracy with good precision, recall, and F1-score, particularly for the positive class. Tourist satisfaction factors included natural beauty (28.46%), community friendliness (22.38%), affordable prices (18.92%), cleanliness (15.67%), and adequate facilities (14.57%). Dissatisfaction concerned accessibility and road conditions (31.71%), inadequate facilities (26.83%), site cleanliness (19.51%), and entrance ticket prices (14.23%). Conclusion: Text-mining-based sentiment analysis effectively captures tourist perceptions and identifies priorities for tourism village improvement, particularly infrastructure, facilities, cleanliness, and community service training for satisfying and sustainable tourism experiences.]]>
Copyrights © 2026
