LAUSR

Abstract Online tourism reviews provide a crucial source of information for the tourism industry, and determining whether they can be effectively identified is key to influencing tourism decision-making. The purpose of this paper is to identify themes and compare differences in online travel reviews. A semantic association analysis was applied to extract thematic words and construct a semantic association network from 165,429 reviews obtained from three major online travel agencies (OTAs) in China. The findings show that there are apparent discrepancies on these platforms in terms of thematic words, the distribution of topics, structural properties and community relationships. In particular, the results of network visualization can clearly identify hot topics and the social network relationships of thematic words. The proposed analytical framework expands our understanding of the methodological challenges and offers novel insights for mining the opinions for the benefit of tourists, hotels and tourism enterprises and OTAs.