1 Faculty of Humanities and Social Sciences, Burapha University, Chon Buri, Thailand
2 Faculty of Environment and Resource Studies, Maha Sarakham University, Maha Sarakham, Thailand
3 International College, Burapha University, Chon Buri, Thailand
Corresponding Author: narong_p@go.buu.ac.th
DOI : https://doi.org/10.36647/GPISET/2026.01.B1.Ch001
This study develops a rapid, data-driven approach to detect operational sustainability gaps in island tourism by applying machine-learning sentiment analysis to user-generated TripAdvisor reviews of Koh Lan (2015–2024). A corpus of 2,388 English- language reviews was preprocessed and classified with five supervised algorithms; a Support Vector Machine (SVM) produced the best performance (accuracy 87.29%, F1 75.38%). Although the majority of reviews were positive (91.57%), a small negative subset (90 reviews, 4.25%) contained concentrated, actionable signals. Qualitative coding of negative reviews revealed five recurring problem dimensions—Scenery (37.29%), Staff and Service (29.38%), Facility (19.21%), Safety (10.73%), and Accessibility (3.39%)—yielding 179 distinct issue mentions. Each identified problem was mapped onto specific SDG targets and assessed against the Blue Economy pillars (environmental sustainability, economic resilience, and social equity), exposing persistent weaknesses in marine and waste management, infrastructure resilience, service quality, and institutional governance. The paper translates these diagnostics into a pragmatic “Blue Tourism” policy package—zero-plastic and blue-carbon initiatives, eco-infrastructure upgrades, eco-service certification, and a participatory marine governance and risk committee—to shift Koh Lan from a short-term, extractive tourism model toward regenerative stewardship. Methodologically, the study demonstrates that ML-enabled monitoring of online reviews can provide timely, high-resolution intelligence to guide sustainable destination management.
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Technoarete Publishers
978-93-92104-70-1
https://doi.org/10.36647/GPISET/2026.05.01.Book1
