Home // HUSO 2021, The Seventh International Conference on Human and Social Analytics // View article
Authors:
Alexandros Bousdekis
Dimitris Kardaras
Stavroula Barbounaki
Keywords: e-tourism; data analytics; machine learning; tourism management; service quality
Abstract:
Online review comments have become a popular and efficient way for sellers to acquire feedback from customers and improve their service quality. These online reviews in the e-tourism era, in the format of both textual reviews (comments) and ratings, generate an electronic Word Of Mouth (eWOM) effect, which influences future customer demand and hotels’ financial performance, and thus, have significant business value. This paper proposes an approach for hotel quality evaluation according to online review comments and ratings using Fuzzy String Matching (FSM) for mining customers’ opinions and Bayesian Belief Networks (BBN) for evaluating the attributes that contribute to the review rating. The proposed approach was applied to a dataset from TripAdvisor. The results show that the proposed approach is able to model the complex dynamics of online hotel review data, which are derived from both the textual nature of the review comments and the uncertain relationships between these comments and the review rating.
Pages: 24 to 29
Copyright: Copyright (c) IARIA, 2021
Publication date: July 18, 2021
Published in: conference
ISSN: 2519-8351
ISBN: 978-1-61208-884-6
Location: Nice, France
Dates: from July 18, 2021 to July 22, 2021