Authors - Criscel Jay F. Nayve, Lord Francis B. Navarro,Karen Aparicio Doblas, Elvan Budiongan, Darrel A. Cardana, Max Angelo D. Perin Abstract - This study evaluates online tourist feedback on the Chocolate Hills in Carmen, Bohol, Philippines, using Natural Language Processing (NLP) techniques. Although the destination consistently receives high ratings, negative reviews contain critical insights that can guide tourism management. A total of 4,059 Google Maps reviews were collected, of which 2,011 contained textual content suitable for analysis. The dataset underwent preprocessing using Python and Orange Data Mining before applying sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling. Results show that, while overall sentiment toward the Chocolate Hills remains strongly positive, negative reviews highlight key concerns related to accessibility, and crowding. Topic modeling identified five dominant themes: scenic appreciation, environmental ambience, crowd density and photo-taking behavior, physical effort required for climbing viewpoints, and perceived cost–benefit value. Sentiment trends from 2020 to 2025 indicate stable positive perceptions despite pandemic-related fluctuations in review volume. Findings suggest that tourists’ satisfaction is primarily driven by the site’s natural beauty, but logistical challenges require targeted management interventions. The study contributes to localized tourism analytics in the Philippines and demonstrates the usefulness of NLP for extracting actionable insights from large volumes of user-generated content.
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