Hu Tao, Geng Juan
School of Tourism, Hainan University, Haikou, China.
PeerJ Comput Sci. 2024 Jan 31;10:e1801. doi: 10.7717/peerj-cs.1801. eCollection 2024.
Destination image is a powerful means by which destinations compete in the tourism industry, and the accurate identification of a destination image better serves destination marketing and management. This study uses multimodal data, such as text, images, and videos uploaded by tourists, to construct a comprehensive and systematic destination image process. The "cognitive-emotional-overall image" model, latent Dirichlet allocation (LDA) model, and deep residual neural networks are implemented to build a framework to examine the perception of a destination image, travelogues, and short videos from the sources called Ctrip, Qunar, and TikTok. The results show that tourists' overall perception of Sanya is based mainly on the cognitive image of natural scenery, human resources, and food. In addition, there are differences between textual and visual cognitive images among the perceptual images when multimodal data is under consideration. Furthermore, tourists have an overall positive affective image of Sanya as a destination.
目的地形象是目的地在旅游业中竞争的有力手段，准确识别目的地形象能更好地服务于目的地营销与管理。本研究使用多模态数据，如游客上传的文本、图像和视频，构建一个全面系统的目的地形象塑造过程。运用“认知-情感-整体形象”模型、潜在狄利克雷分配（LDA）模型和深度残差神经网络，构建一个框架，以考察来自携程、去哪儿和抖音等平台的目的地形象、旅行日志和短视频的感知情况。结果表明，游客对三亚的整体感知主要基于自然风光、人力资源和美食的认知形象。此外，在考虑多模态数据时，感知图像中的文本和视觉认知形象之间存在差异。此外，游客对三亚作为一个目的地有着整体积极的情感形象。
