Multi-Label Classification of Foreign Tourists' Opinions on Thailand Tourism Development
Lalita Suramanka, Anantaporn Hanskunatai · 2024
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.