Analysis of User-Generated Content in Visitor Reviews of Tourist Attractions Using Semantic Similarity
TL;DR
An integrated framework combining Topic-RoBERTa, topic-level semantic network analysis, and Graph Attention Networks (GAT) to extract experience topics and model their semantic associations and offer valuable insights for tourism management and service optimization is presented.
Abstract
Online tourist reviews, a major form of user-generated content (UGC), are often short and unstructured, complicating the identification of tourist experience dimensions and their relationships. This study presents an integrated framework combining Topic-RoBERTa, topic-level semantic network analysis, and Graph Attention Networks (GAT) to extract experience topics and model their semantic associations. The authors validated the framework on 186,429 reviews from the OD-TripM TripAdvisor review dataset released by the Data Science and Computational Intelligence (DaSCI) research group on GitHub and the Yelp Open Dataset, identifying 12 tourist experience dimensions and constructing a community-structured semantic network. The results show that the proposed method can extract interpretable topics, reveal fine-grained attention-weighted associations, and provide a more structured understanding of tourist experiences. The findings offer valuable insights for tourism management and service optimization.