Revolution trend investigation of tourism destination image with machine learning

Abstract

In tourism destination image research, topic modeling, mutual relationships among keywords, and image evolution trends have received limited attention. Filling these gaps in the extant literature, based on tourist gaze theory, this study is the first to use Latent Dirichlet Allocation, text visualization, and topic evolution trend methods to explore the Dunhuang Mogao Grottoes’ image. Analyzing 22,371 reviews posted on tourism social networks, we find that (1) prominence, uniqueness, a rich culture, convenience, and enormous influence are five dimensions of the Dunhuang Mogao Grottoes’ image; (2) these dimension keywords display different cooccurrence relationships; and (3) the dimension evolution trends vary in direction and degree. Our study enriches existing literature and provides important practical implications for the destination marketing field.