Text Sentiment Analysis for Thailand Tourism Recommendation

Abstract

Thailand is one of the world's most popular travel destinations. In the digital age, online reviews written by previous travelers have become a highly influential information source for decision-making. Unfortunately in many cases, users do not assign ratings, or the platforms do not require them. As a result, these systems are unable to process sentiment effectively from the textual reviews. Moreover, most existing recommendation systems lack the ability to group destinations with similar characteristics or categories. This paper proposes a tourism recommendation system that integrates sentiment analysis with tag clustering. The system is capable of processing both user sentiment and destination-related content, enabling it to generate personalized recommendations that align with users' preferences and emotional context. The experimental results show that the SVM model achieved an average sentiment classification accuracy of 94%. In contrast, tag clustering using DBSCAN presented a limitation in the form of high noise levels-over 30% of tags were classified as noise (represented by cluster -1), indicating that a significant number of tags could not be assigned to any meaningful cluster. This reflects a limitation in tag coverage and suggests that further refinement is needed to improve grouping performance in real-world datasets with diverse and contextually ambiguous descriptions.