Person:
Portela García-Miguel, Javier

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First Name
Javier
Last Name
Portela García-Miguel
URI
https://hdl.handle.net/20.500.14352/77436
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Estudios estadísticos
Department
Estadística y Ciencia de los Datos
Area
Estadística e Investigación Operativa
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Author: Portela García-Miguel, Javier × Subject: 338.48 ×

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Now showing 1 - 2 of 2
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    A model integrating the 2‑tuple linguistic model and the CRITIC‑AHP method for hotel classification
    (SN Computer Science, 2023) Shu, Ziwei; Carrasco González, Ramón Alberto; Portela García-Miguel, Javier; Sánchez‑Montañés, Manuel; Pal, Umapada; Yuen, Chau
    Hotel classification is essential for hotel managers and customers. It can assist hotel managers in better understanding the needs of their customers and in improving various aspects of the hotel through relevant strategies. It also aids customers in choosing appropriate accommodations according to their preferences regarding hotel location, services, and other aspects. This paper aims to improve our previous model by incorporating expert opinions into the weight calculation, thereby increasing its practical applicability. The extended model combines the analytical hierarchy process (AHP) and the CRiteria Importance Through Intercriteria Correlation (CRITIC) methods, introducing a novel approach for calculating the weights of each aspect. The 2-tuple linguistic model is retained in the extended model to resolve the problem of information loss in linguistic information fusion. Finally, various hotel segments are obtained with the weighted K-means clustering. A dataset with over fifty million hotel reviews from TripAdvisor has been applied to evaluate the extended model. The results show that the extended model achieves denser and better separated hotel clusters than our previous model, while maintaining the same advantages. This model is more likely to help hotel managers create better strategies to tackle hotel weaknesses or gain competitive advantages, as it combines two types of weights that improve clustering results: the quantity of information in each hotel aspect and the expert judgment of each aspect's importance in hotel development.
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    Clustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: the case of TripAdvisor
    (Expert Systems with Applications, 2023) Shu, Ziwei; Carrasco González, Ramón Alberto; Portela García-Miguel, Javier; Sanchez-Montañés, Manuel; Lin, Binshan
    With the growth of online tourism, it is important to analyze the reviews left by numerous customers on social networks to improve the hotel’s online reputation. Hotel segmentation based on online reviews has attracted an increasing interest from many academics. The problem is that many hotel segmentation models overlook the fact that some customers focus on positive reviews when choosing a hotel, while others focus on negative ones. To address this shortcoming, this paper develops a novel approach to classify hotels using the ordered weighted averaging (OWA) operator, the 2-tuple linguistic model, and K-means clustering. The proposed approach has been evaluated with a real dataset from TripAdvisor, which contains more than 50 million customer online reviews on eight aspects of the hotel. The results show that the proposed model can produce denser and more separated clusters than the model without linguistic quantifiers. From a business point of view, this model enables hotels to distinguish customers’ perceptions (from the less demanding to the most demanding) about their eight aspects, allowing them to specify which of them need to be improved and develop strategies more quickly. At the same time, it introduces a new way of ranking hotels online, allowing customers to create personalized rankings of hotels based on their degree of demand for various hotel aspects (better location, cleaner rooms, etc.) rather than the average ratings, so that they can select the most suitable hotels more quickly.