A Study on Perceptual Differences in Tourist User-Generated Content Based on Multimodal Deep Learning
本研究利用主题建模和深度学习情感分类，分析北京环球影城和上海迪士尼的微博与小红书帖子，发现文本、图文和视频三种模态的用户生成内容在感知主题和情感分布上存在显著差异，为旅游管理提供具体建议。
This study examines how tourist perceptions in theme park settings are shaped by three modalities of user-generated content: text, image-text, and video. Drawing on Media Richness Theory, we analyze posts from Weibo and RedNote related to Universal Beijing Resort and Shanghai Disneyland using topic modeling and deep learning sentiment classification. Seven perceptual themes emerge, with clear cross-modal differences: text emphasizes service evaluation, image-text highlights aesthetics and symbolic elements, and video captures immersive, process-based narratives; sentiment distributions also differ by modality. Theoretically, the findings refine media-task fit by showing stable correspondences between modality cues and the focus of perceptual expression. Methodologically, the study demonstrates a scalable pipeline for mining multimodal perceptions from large, real-world corpora. Practically, the results translate into concrete measures: deploy text monitoring to surface operational issues and reply with concise guidance; standardize visual presets and required hashtags to strengthen brand visibility; and curate first-person video with authentic on-site sound to amplify atmosphere around characters and rides.