Gender difference on destination image and travel options: An exploratory text-mining study

Destination image has gained increasingly attention of tourism researchers. However, little attention has been devoted to study the difference in perception of destination image among different demographic groups using the big data analytics in general, the text mining approach in particular. Therefore, in this paper, we develop a method based on Latent Dirichlet Allocation (LDA) to extract cognitive destination image from travel blogs. Furthermore, we explore the differences in cognitive image and travel options among different gender groups. Through experiments, we extract 26 topics by LDA and find that the female has significantly preferences to natural landscape and rural scenery than the male. Meanwhile, the male prefers historical sites than the female. In terms of the travel options, we find the female usually travel with travel agency, while the male travel by self-drive. Our study contributes to the tourism planning, tourism positioning and marketing for different target groups.