4.6 Article

Fusing hotel ratings and reviews with hesitant terms and consensus measures

Journal

NEURAL COMPUTING & APPLICATIONS
Volume 32, Issue 19, Pages 15301-15311

Publisher

SPRINGER LONDON LTD
DOI: 10.1007/s00521-020-04778-x

Keywords

Hesitant fuzzy linguistic term sets; Linguistic decision making; Consensus models; Tourism; Reviews

Funding

  1. Department of Enterprise and Knowledge of the Generalitat de Catalunya [2017 DI 086]
  2. INVITE Research Project - Spanish Ministry of Science and Information Technology [TIN2016-80049-C2-1-R, TIN2016-80049-C2-2-R]

Ask authors/readers for more resources

People have come to refer to reviews for valuable information on products before making a purchase. Digesting relevant opinions regarding a product by reading all the reviews is challenging. An automated methodology which aggregates opinions across all the reviews for a single product to help differentiate any two products having the same overall rating is defined. In order to facilitate this process, rating values, which capture the overall satisfaction, and written reviews, which contain the sentiment of the experience with a product, are fused together. In this manner, each reviewer's opinion is expressed as an interval rating by means of hesitant fuzzy linguistic term sets. These new expressions of opinion are then aggregated and expressed in terms of a central opinion and degree of consensus representing the agreement among the sentiment of the reviewers for an individual product. A real case example based on 2506 TripAdvisor reviews of hotels in Rome during 2017 is provided. The efficiency of the proposed methodology when discriminating between two hotels is compared with the TripAdvisor rating and median of reviews. The proposed methodology obtains significant differentiation between product rankings.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available