Extracting Information on Aspects of Sustainable Tourism in ASEAN Using Named Entity Recognition (NER)

Abstract

Sustainable tourism is an important issue in the ASEAN region, which has experienced rapid growth in the tourism sector but faces challenges in maintaining a balance between economic, social, and environmental aspects. Information on sustainability practices is scattered across various forms of text, making it difficult to analyze manually. This study aims to extract information on aspects of sustainability in tourism using a transformer-based Named Entity Recognition (NER) approach. Three data sources were used: government websites, online news, and travel reviews on TripAdvisor. Five transformer models were compared, namely BERT, ALBERT, DistilBERT, ELECTRA, and RoBERTa, to evaluate entity extraction performance. The dataset was divided using an 80:10:10 ratio for training, validation, and testing. The results showed that DistilBERT provided the best performance with a balance of accuracy and computational efficiency. In addition, an analysis of the distribution of sustainability aspects in ASEAN countries and Indonesia in particular was conducted to identify practices that have already been implemented. These findings are expected to contribute to the development of more sustainable tourism policies and practices in the ASEAN region and Indonesia.