Exploring Coastal Tourism Experience Through Social Media Text Mining: Sentiment and Thematic Patterns

Abstract

Research on coastal recreational activities has grown substantially, yet studies focusing on user perceptions of these spaces—critical for optimizing tourism experiences and management—remain fragmented and underdeveloped. This study addresses this gap by examining tourist sentiment in Xiamen, a renowned coastal city in China, using social media data. Text mining tools were utilized to process the Weibo contents through text segmentation, frequency analysis and cluster analysis. The Two-way Neural Network Fusion Model Based on the BERT (TNNFMB) deep learning approach was employed using transfer learning for sentiment analysis, while the Latent Dirichlet Allocation (LDA) model was used to uncover latent thematic patterns. Sentiment polarity analysis revealed that positive comments constituted 56.47%, negative comments only 16.3%, and neutral comments 27.2%, confirming a generally positive perception of visitors’ coastal experiences. Tourists’ social media posts primarily revolve around five core themes in coastal areas: coastal waters, waterfronts, adjacent environments, culture and creativity, and reputation and expectation. The spatial and temporal changes in sentiment scores were discovered. Areas emphasizing sea–land landscapes, cultural theme reinforcement, and open public activities generally achieved high and stable sentiment scores. Natural and natural–artificial mixed coastlines experienced significant seasonal variations in sentiment. The recommendations of this study, generated from a sentiment perspective, include shaping a harmonious coastal environment by improving coastal management and support services to enhance the comfort of the tourist experience. This study advances understanding of user-centric coastal tourism dynamics, providing evidence-based tools for managers to enhance tourist experiences and spatial quality.