Sentiment analysis of tourist reviews on google maps for pura besakih using machine learning algorithms
DOI:
https://doi.org/10.35335/mandiri.v14i1.449Keywords:
Cultural Heritageh, Machine Learning, Pura Besaki, Tourist ExperienceAbstract
Tourist reviews on digital platforms have become a valuable source of information for understanding visitor experiences. This study applies sentiment analysis to 2,891 Google Maps reviews of Pura Besakih, Bali’s largest and most sacred temple, collected between January 2023 and December 2024. The aim is to assess overall visitor sentiment and identify factors influencing satisfaction and dissatisfaction. Reviews were preprocessed using a standardized pipeline that included translation, cleaning, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using the Indonesian Sentiment Lexicon (InSet), followed by classification using six machine learning models: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Random Forest, and Neural Network. The SVM model achieved the highest performance with an accuracy of 76.3% and F1-score of 55.68%. Thematic analysis revealed positive feedback highlighting the temple’s spiritual ambiance, architecture, and improved facilities, while negative sentiment was driven by issues such as unauthorized guides, misleading charges, and restricted access. These findings offer valuable insights for tourism stakeholders to improve visitor experience and support sustainable heritage tourism through data-driven decision-making.
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