The tourism and hospitality industry, particularly the restaurant business, has been greatly affected by the COVID-19 pandemic. To comprehend customer behavior and preferences during this unprecedented time, it is crucial to analyze online restaurant customer reviews. Thus, this study utilized the valence aware dictionary for sentiment reasoning (VADER) model to examine TripAdvisor reviews of restaurants in Pattaya City, Chon Buri, Thailand, covering the period 2017–2022, which encompasses both pre-pandemic and pandemic years. The findings reveal a significant decrease in the number of reviews and a notable increase in negative sentiments during the COVID-19 pandemic compared to normal circumstances. We noticed two concern areas, i.e., service and staff, and food and taste, that should be addressed urgently. The findings of this study offer valuable insights into customer behavior and requirements, thereby empowering restaurant businesses to enhance service quality, satisfy customer requirements, and strategically plan for a post-COVID-19 future.

This study presents the distribution of road accidents in Bangkok, Thailand, during the COVID-19 outbreak (162 days) using spatial statistics. The findings show that the number of road accidents fell by 28.53% during the lockdown period (March 26 to June 14, 2020) compared to before the lockdown period (January 5 to March 25, 2020). There was also a considerable decrease in road accidents by approximately 4.96% compared to the same period in 2019 (January 4 to June 14, 2019). High accident incidence areas were located in central and southwestern Bangkok, and this pattern was also observed before and during the lockdown as well as in 2019. The findings revealed the numbers and patterns of road accidents that occurred during the COVID-19 outbreak. This information can be useful in minimizing and limiting the impact and damage caused by road accidents in Bangkok. Keywords: COVID-19; lockdown; road accident; Moran’s I; spatial autocorrelation *Corresponding author: Tel.: (+66) 822313386 Fax: (+66) 38102379 E-mail: narong_p@buu.ac.th

Tourism destinations increasingly serve both domestic and international visitors whose geographic behaviors may differ substantially, yet most analytical frameworks treat visitor distributions as spatially homogeneous. Few studies compare how domestic and international tourists cluster spatially within the same destination. Those differences matter enormously for destinations where visitor segments follow distinct geographic patterns. We analyzed 1547 bilingual TripAdvisor reviews from Chanthaburi Province, Thailand (2014–2023), combining Random Forest classification (83.26% accuracy for Thai, 96.45% for English) with Incremental Spatial Autocorrelation (ISA), Global Moran’s I, and Getis-Ord Gi* hotspot analysis. International visitors clustered more intensely overall (I = 0.253 vs. 0.213), but domestic visitors spread across all six tourism areas including agrotourism, while international visitors were concentrated in heritage, coastal recreation, and nature-temple zones with agrotourism absent. Both segments clustered strongly at cultural heritage sites and at beach destinations, contradicting the common assumption that coastal areas primarily serve international visitors, while agrotourism clustered exclusively among domestic visitors despite active policy promotion. These patterns reflect differential information access rather than attraction quality. The zone-level framework is transferable to secondary heritage destinations across Southeast Asia, where platform-based monitoring offers a practical alternative to large-scale visitor surveys.

Tourism significantly contributes to the economic development of regions with rich natural and cultural heritage, such as Chanthaburi Province, Thailand. This study leverages Natural Language Processing (NLP) and the Random Forest machine learning algorithm to analyze 1,547 tourist reviews from TripAdvisor, spanning 2014–2023. The reviews, written in Thai and English, were categorized into positive, neutral, and negative sentiments, achieving classification accuracies of 83.26% and 96.15%, respectively. Keyword frequency analysis using Term Frequency-Inverse Document Frequency (TF-IDF) highlighted key themes influencing tourist experiences, including appreciation for cultural landmarks and concerns about overcrowding and ethical issues related to wildlife performances. The findings provide valuable insights for tourism stakeholders in Chanthaburi Province to enhance visitor experiences and develop targeted improvement strategies. The study demonstrates the effectiveness of machine learning approaches in analyzing multilingual tourist feedback for destination management and marketing optimization.