Abstract
Online travel reviews offer valuable data, yet it remains uncertain if those most influenced by these reviews actually read them. This research aims to uncover consistent patterns and explain variations in online travel ratings, comments, and reviews. To accomplish this, millions of reviews were collected from Pakistan's top online travel companies, Uber and Careem. Utilizing semantic affiliation analysis, subject terms were extracted, forming a semantic affiliation structure. The findings highlight significant differences among channels concerning topical vocabulary, subject distribution, structural traits, and community links. The network visualization results are particularly noteworthy, as they illustrate connections between key concepts and words within each topic, making them easily understandable. With the proposed logical method, we can better understand the strategic snafus in the travel sector and gain fresh insights into how to dig up popular assessments to better serve tourists, lodging establishments, and trade groups.
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