Air Quality, Heat, and Visitor Experience in Lumpini Park, Bangkok: An AI-Enabled Evaluation of an Iconic Urban Park

Urban parks provide recreation, cultural ecosystem services, microclimate regulation, and benefits for well-being, but their realized value may vary with air pollution and heat. This exploratory study linked 2947 Google Maps reviews posted in 2024–2025 to daily PM2.5, temperature, humidity, and precipitation and assessed a BERT-based workflow for classifying star-linked sentiment and dominant emotion text labels. Star-linked sentiment classifications were highly favorable (mean score of 4.86/5; 88.39% very positive). Independent weighting of the rounded air-quality and heat-stress subgroup summaries yielded consistent approximate overall shares for provisional joy (28.9%) and anger (32.5%), but the full seven-class distribution could not be reconstructed from the archive. All emotion-label analyses remain provisional. All 25 tested environmental correlations were negligible (absolute Spearman ρ ≤ 0.049), and none met the Bonferroni-corrected significance threshold (α = 0.002). Posting dates classified as heat-stress days showed descriptive differences in provisional anger and joy labels, but posting date is an uncertain proxy for the visit date. The findings do not establish individual-level exposure associations. They instead illustrate the methodological safeguards needed before user-generated park feedback and environmental monitoring can support climate-responsive management.