A large scale group decision making system based on sentiment analysis cluster.

Nowadays, group decision making is an everyday occurrence in different scenarios, such as marketing or social networks. These social networks have facilitated communication between experts because they do not need to meet in person. However, communication between experts through the internet generates three problems: the management of large amounts of information, the fact that experts often provide their information using natural language, and the lack of analysis of experts' intentions. In this paper, we propose a novel large scale group decision making method to manage the information generated by a large number of experts, using the natural language processing approach, specifically sentiment analysis. This approach makes it possible to detect the degree of positivity and aggressiveness of each expert and thus proceed to a classification. Once the behaviours are detected, the experts are grouped according to them and, for each group, a weight and a unique preference relation is obtained. In addition, we propose an optimised consensus analysis process, in which it is not necessary to compare all experts with each other, but only groups of experts. • We created a classifier to detect aggressiveness and positivity. • Experts are clustered according to their aggressiveness and positivity. • The expert groups are used to create an LGDM system. • The LSGDM system determines a ranking of alternatives agreed upon by the experts. [ABSTRACT FROM AUTHOR]