Multi-Label Classification of Foreign Tourists' Opinions on Thailand Tourism Development

Abstract

The enhancement of tourism quality in Thailand through the understanding and utilization of foreign tourists' opinions presents challenges due to the extensive volume of data involved. This research proposes a two-fold approach to address this issue: (1) the development of an opinion classification model, and (2) the analysis of tourists' opinions through a dashboard. A dataset, compiled by the Tourism Authority of Thailand (TAT) and consisting of opinions from foreign tourists regarding areas for improvement in Thai tourism, was utilized. A total of 2,249 comments were collected. Experimental results demonstrate that the use of data augmentation, feature selection, Multi-label transformation using Classifier Chains, and the Random Forest classification model on the training dataset yields promising results with an accuracy rate of 80%, precision of 90%, recall of 81%, F1-score of 85%, and Hamming loss of 0.04. Analysis from the dashboard revealed the top three key areas for improvement: communication/language, traffic/public transportation, and cleanliness/hygiene.