Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 56-68
Abstract
This independent study aims to understand the behaviors of tourists affect-ed by hostel accommodation services in a Mueang Chiang Mai district, Chiang Mai province. The data used in this research was collected from TripAdvisor.com, 5,108 messages were crawled and separated into 17,092 sentences. In-depth interviews with hostel entrepreneurs provide insights for the essential aspects considered when managing their businesses. These aspects together with aspects from related studies serve as classification cri-teria for sentiment analysis using Support Vector Machine (SVM) and Mul-tinomial Naïve Bayes (MNB) algorithms. The SVM model achieves 93% ac-curacy, outperforming MNB's 82%. Text-mining analysis explores hostel business development. The findings reveal that SVM is suitable for classi-fying customer review messages, and exhibiting satisfactory performance and accuracy. The aspects discovered in this studies include cleanliness, fa-cility, location, quality of staff, security, social atmosphere, and value of money. The results of the current study contribute to the theoretical context for academic as well as practical guidelines for the hostel managers in gen-eral.