Exploring Online Travel Reviews Using Data Analytics: An Exploratory Study
Vera L. Miguéis () and
Henriqueta Nóvoa ()
Additional contact information
Vera L. Miguéis: Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
Henriqueta Nóvoa: Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
Service Science, 2017, vol. 9, issue 4, 315-323
Abstract:
The information provided by online traveler reviews is becoming a key element in the decision-making process of hotel customers, reducing the uncertainty and the perceived risk of a traveler. Therefore, a careful analysis of the content provided by online customers’ reviews might give invaluable information concerning the key determinants, from a user’s perspective, of the quality of the service provided, justifying the attributed service rating. The objectives of this study are twofold: (1) use text-mining techniques to analyze the user’s generated content automatically collected from hotels in Porto in a certain period of time and, from this analysis, derive the most frequent terms used to describe the service; (2) understand whether it is possible to predict the aggregated rating assigned by reviewers based on the terms used and, at the same time, identify the terms showing high predictive capacity. Our study attempts to support hotel service managers in achieving their strategic and tactical goals by using innovative text- and data-mining tools to explore the wealth of information provided by user generated content in an easy and timely way.
Keywords: hotel service quality; user-generated content; text mining (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1287/serv.2017.0189 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:9:y:2017:i:4:p:315-323
Access Statistics for this article
More articles in Service Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().