Destination image analysis and marketing strategies in emerging panda tourism: a cross-cultural perspective

The burgeoning panda tourism market in China is attracting an increasing number of domestic and international tourists. This study focuses on the Chengdu Research Base of Giant Panda Breeding as a case study and utilizes Latent Dirichlet Allocation (LDA) modeling and topic-based sentiment analysis to conduct text mining on online travel reviews in both English and Chinese languages. LDA modeling was employed to identify topics within online reviews, with a subsequent evaluation of the importance of each topic. Furthermore, topic-based sentiment analysis was conducted to assess the performance of different topics. Through importance-performance analysis, this study interprets the destination image disparities between English and Chinese reviews from a cross-cultural perspective. The research findings validate the effectiveness of destination image analysis methods, providing valuable insights for tailoring distinct destination marketing strategies that target tourists from diverse linguistic backgrounds.