A linguistic multi-criteria decision making methodology for the evaluation of tourist services considering customer opinion value

Asa consequence of the exponential growth in online data, tourism sector has experimented a radical transformation. From this large amount of information, opinion makers can be benefited for decision making in their purchase process. However, it can also harm them according to the information they consult. In fact, being benefited or harmed by the information translates into greater or lesser satisfaction after the purchase. This will largely depend on the published opinions that they take into account, which in turn depend on the value of the opinioner who publishes said information. In this paper, the authors propose a methodology that integrates multiple decision-making techniques and with which it is intended to obtain a ranking of hotels through the opinions of their past clients. To do this, the customer value is obtained using the Recency, Frequency, Helpfulness model. The information about the users found in the social networks is managed and aggregated using the fuzzy linguistic approach 2-tuples multi-granular. In addition, we have verified the functionality of this methodology by presenting a business case by applying it on TripAdvisor data. (C) 2020 The Author(s). Published by Elsevier B.V.

Automated summary

The exponential growth in online data has led to a radical transformation in the tourism sector. Decision makers can benefit or be harmed by the large amount of information available, which can impact their satisfaction after purchase. A methodology integrating multiple decision-making techniques was proposed to rank hotels based on past client opinions, using the Recency, Frequency, Helpfulness model and a fuzzy linguistic approach. This methodology was verified through a business case applied to TripAdvisor data.