Harnessing BERT for Semantic Understanding in Tourism Recommendation Engines

It will be necessary for attraction managers within hotels to track guests' lifestyles to keep the business running. Such an understanding may be achieved, for example by analyzing reviews on attractions to capture the attitudes of the visitors towards the services and business within the tourism industry. The approach utilizes web scraping to gather user-generated reviews, using text preprocessing, data pre-processing, and further improvement of the model using labelled sentiment data divided into three sentiment classes: positive, negative, or neutral. The dataset consisting of 908 reviews were divided in 70:15:15 ratio for training, validation and testing. Model performance was measured in terms of accuracy, precision, recall and F1-score. In this study, the BERT deep learning model is used to classify sentiments of Indonesian tourist. Using the SmallBERT variant fine-tuned on 515k reviews for 5 epochs, the model achieved 91.40% accuracy, 90.51% precision, recall, and F1 score. The results indicate a dominance of positive sentiments, visualized using tableau. This research provides a robust foundation for developing intelligent sentiment-based recommendation systems in the tourism sector and suggests future exploration using other transformer-based models such as GPT, T5, or BART for comparative analysis.