Harnessing BERT for Semantic Understanding in Tourism Recommendation Engines

Harnessing BERT for Semantic Understanding in Tourism Recommendation Engines

BERT; classification; deep learning; fine-tuning; NLP

It will be necessary for attraction managers withinhotels to track guests' lifestyles to keep the business running. Such an understanding may be achieved, forexample 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, datapre-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, validationand testing. The effectiveness of the model was evaluated using accuracy, precision, recall, and the F1-score. In this study, theBERT deep learning model is used to classify sentiments of Indonesian tourist. Using the SmallBERT variant fine-tuned on515k 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 intelligentsentiment-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

Renita Astri1*, Lai Po Hung2, Suaini Binti Sura3, Ahmad Kamal

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6575/1120

Sistem Informasi, Fakultas Farmasi Sains dan Teknologi, Universitas Dharma Andalas, Padang, Indonesia

August 19, 2025

FAJAR BAGUS W

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