Authors:
Ziwei Shu
1
;
Ramón Alberto Carrasco González
2
;
Javier Portela García-Miguel
1
and
Manuel Sánchez-Montañés
3
Affiliations:
1
Department of Statistics and Data Science, Faculty of Statistics, Complutense University of Madrid, Avenida Puerta de Hierro, s/n, 28040 Madrid, Spain
;
2
Department of Management and Marketing, Faculty of Commerce and Tourism, Complutense University of Madrid, Avenida de Filipinas, 3, 28223 Madrid, Spain
;
3
Department of Computer Science, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Keyword(s):
Hotel Classification, Weighted K-means Clustering, 2-tuple Linguistic Model, CRITIC Method, Multi-criteria Decision Making.
Abstract:
Hotel classification is critical for both customers and hotel managers. It can help hotel managers better understand their customers’ needs and improve their various aspects by implementing relevant strategies. Moreover, it can assist customers in recognizing different hotel aspects and making a more informed decision. This paper categorizes hotels on TripAdvisor based on their six aspects. The 2-tuple linguistic model is applied to solve the problem of information loss in linguistic information fusion. The CRiteria Importance Through Intercriteria Correlation (CRITIC) approach is employed to generate objective weights to calculate the overall score of each hotel, as this method does not require any human participation in the weighting computation. Finally, various hotels segments are obtained with Weighted K-means clustering. This proposal has been evaluated by a use case with more than fifty million TripAdvisor hotel reviews. The results demonstrate that the proposed model can incr
ease the linguistic interpretability of clustering results and provide customers with a more understandable objective overall hotel score, which can assist them in selecting a better hotel. Moreover, these classification results aid hotel managers in designing more effective tactics for acquiring a new competitive advantage or enhancing those aspects that require improvement.
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