TY - JOUR
T1 - Fusing hotel ratings and reviews with hesitant terms and consensus measures
AU - Nguyen, Jennifer
AU - Montserrat-Adell, Jordi
AU - Agell, N.
AU - Sánchez, Monica
AU - Ruiz, Francisco J.
N1 - Funding Information:
This research has been partially supported by the Secretary of Universities and Research of the Department of Enterprise and Knowledge of the Generalitat de Catalunya (2017 DI 086) and by the INVITE Research Project (TIN2016-80049-C2-1-R and TIN2016-80049-C2-2-R (AEI/FEDER, UE)), funded by the Spanish Ministry of Science and Information Technology.
Funding Information:
This research has been partially supported by the Secretary of Universities and Research of the Department of Enterprise and Knowledge of the Generalitat de Catalunya (2017 DI 086) and by the INVITE Research Project (TIN2016-80049-C2-1-R and TIN2016-80049-C2-2-R (AEI/FEDER, UE)), funded by the Spanish Ministry of Science and Information Technology.
Publisher Copyright:
© 2020, Springer-Verlag London Ltd., part of Springer Nature.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - 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.
AB - 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.
KW - Consensus models
KW - Hesitant fuzzy linguistic term sets
KW - Linguistic decision making
KW - Reviews
KW - Tourism
UR - https://www.scopus.com/pages/publications/85081377589
U2 - 10.1007/s00521-020-04778-x
DO - 10.1007/s00521-020-04778-x
M3 - Article
AN - SCOPUS:85081377589
SN - 0941-0643
VL - 32
SP - 15301
EP - 15311
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 19
ER -