A data-driven approach to exploring similarities of tourist attractions through online reviews

Publication Type:

Journal Article

Source:

Journal of Location Based Services (2018)

URL:

http://www.grantmckenzie.com/academics/McKenzieAdams_Tourism2018.pdf

Keywords:

Similarity, topic model, tourist attraction, tripadvisor, user-generated content

Abstract:

The motivation for tourists to visit a city is often driven by the uniqueness of the attractions accessible within the region. The draw to these locations varies by visitor as some travellers are interested in a single specific attraction while others prefer thematic travel. Tourists today have access to detailed experiences of other visitors to these locations in the form of user-contributed text reviews, opinions, photographs, and videos, all contributed through online tourism platforms. The data available through these platforms offer a unique opportunity to examine the similarities and difference between these attractions, their cities, and the visitors that contribute the reviews. In this work, we take a data-driven approach to assessing similarity through textual analysis of user-contributed reviews, uncovering nuanced differences and similarities in the ways that reviewers write about attractions and cities.