Cleanliness, Crowds, and Conservation: A Multi-Method Approach using Sentiment Analysis and Spatial Mapping for Sustainable Tourism Management in Thailand
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Abstract
Natural attractions are among Thailand’s most important tourism resources, drawing a large number of international tourists and contributing substantially to national revenue. However, persistent issues such as overcrowding, pollution, and declining environmental quality remain critical challenges for sustainable management. In the digital era, online reviews on travel platforms strongly influence destination choice, providing a rich source of behavioral insights. This study applies sentiment analysis to 124,066 TripAdvisor reviews of natural attractions across all 77 provinces of Thailand, collected between 2014 and 2023. The reviews were processed using text mining techniques—data cleansing, tokenization, lemmatization, and stop-word removal—and analyzed with the Pysentimiento model, a BERT-based deep learning framework, achieving an overall accuracy of 76% and a precision of 87% on the validation dataset in distinguishing positive, negative, and neutral sentiments. Beyond thematic analysis, this research employs Kernel Density Estimation (KDE) to visualize the geographical concentration of dissatisfaction. Results indicate that beaches and islands received the most negative reviews, with recurring issues related to cleanliness, pollution, overcrowding, and unregulated commercial activities. The KDE analysis further reveals that negative sentiment intensity is highly concentrated in specific coastal hotspots (e.g., Phuket, Krabi, and Pattaya). This study contributes methodologically by demonstrating the integrated effectiveness of large-scale sentiment analysis and spatial mapping in tourism research, effectively addressing the implementation gap, providing actionable, precision-based insights for the National Tourism Development Plan (2023–2027). The findings support policymakers and stakeholders in formulating strategies that align with the BCG (Bio-Circular-Green) economic model and balance economic growth with sustainable environmental management by enabling location-specific resource allocation.
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