Parinya Nakpathom

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.

The relationship between customer experience towards customer loyalty in online food delivery services in Thailand has been the subject of a few studies on customers in Generation Z. This study aims to explore the effect of the perception of customer experience, customer satisfaction, and customer loyalty, focusing on Generation Z in online food delivery service. The samples are randomly selected from 400 respondents from Generation Z in Thailand. This analysis uses quantitative research and statistical assessment, including descriptive statistics, Exploratory Factor Analysis (EFA), and Linear Regression Analysis. The result of EFA indicates that the perception of customer experience is identified by 3 factors: namely 1) food quality, 2) convenience and reliability of service, and 3) price. Moreover, the linear regression analysis shows that food quality, convenience and reliability of service, and price are significant motivating factors of customer satisfaction. As a result, customer satisfaction has a positive effect on customer loyalty.