Keywords
BLIP
multimodal analysis
sentence BERT embedding
textual reviews
How to Cite
Abstract
Bali was a prominent global tourism destination and a leading contributor to Indonesia’s tourism revenue. Despite its unique attractions, Bali faced challenges such as overtourism, traffic congestion, and environmental degradation. To explore ways to improve Bali’s tourism, this study analyzed visitor suggestions derived from extensive Google Maps reviews, which were too numerous to be processed manually. A deep learning approach was employed by integrating textual reviews and user-attached images through a multimodal analysis framework. The BLIP Visual Language Model was used to generate captions from tourism images, which were then concatenated with the corresponding textual reviews. The combined text was processed using Sentence BERT embeddings and classified into positive, neutral, and negative sentiments. Subsequently, BERTopic was applied to identify the main discussion themes and generate actionable recommendations. The results revealed nine key topics, including Environmental Quality and Comfort of Tourist Sites, Tourism Infrastructure and Accessibility, and Tourism Safety. The findings provided strategic insights for tourism authorities and stakeholders to identify priority issues, support tourism improvement planning, allocate development resources more effectively, and enhance visitor experience in Bali. In addition, this study demonstrated how multimodal sentiment analysis supported data-driven decision-making for tourism management and policy development.
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