Trust based consensus model for social network in an incomplete linguistic information context

A hybrid approach to the sentiment analysis problem at the sentence level

“…In addition, in the presence of a proper fuzzy logic system, deductions can also be made out of facts expressed in words or determined via the SA approach presented here. In conjunction with social network analysis (SNA) [63,[66][67][68], it could be possible for a company, for marketing purposes for example, to identify the most influential nodes in a network that have very or most positive sentiment towards a particular product [47][48][49].…”

A trust induced recommendation mechanism for reaching consensus in group decision making

“…Other priori factors influencing trust relationship, such as historical interaction and reputation of experts are not considered [38,39]. A potential avenue to explore in future to address this issue is to construct trust relationship by combining a posteriori and a priori trust information to enhance the reliability of trust relationship in the recommendation mechanism in GDM.…”

A new measure of consensus with reciprocal preference relations: The correlation consensus degree

“…Within this framework of preference representation, different consensus measurement based on similarity measures have been put forward by Herrera-Viedma, et al [37] and Wu and Chiclana [63] for both complete and incomplete information environments. The case when experts' opinions are expressed by means of linguistic assessments has been extensively studied and it is worth mentioning the works of Ben-Arieh and Chen [12], Cabrerizo, Alonso and Herrera-Viedma [14], García-Lapresta, Pérez-Román [30], Herrera, Herrera-Viedma and Verdegay [36], Herrera-Viedma, et al [40], Pérez-Asurmendi and Chiclana [53] and Wu, Chiclana and Herrera-Viedma [65]. Finally, models to reach consensus where experts assess their preferences using different preference representation structures (preference orderings, utility functions, multiplicative preference relations and fuzzy preference relations) have also been studied and proposed by Dong and Zhang [23], Fedrizzi et al [26] and Herrera-Viedma, Herrera and Chiclana [39].…”

“…Given a reciprocal preference relation on a set of alternatives, the concept of non-dominance degree introduced by Orlovsky [51] has been extensively used to rank the alternatives [10,23,38,61,63,65,66]. In the following, and in order to improve the understanding of the proposed correlation consensus degree, the consistency of the correlation consensus degree with Orlovsky's non-dominance degree is proved.…”

“…On the one hand, establishing and characterising different methodologies to measure consensus have been addressed from a Social Choice perspective [1,3,13]. On the other hand, within the Decision Making Theory framework, modelling group decision making problems in order to reach a higher level of cohesiveness has been managed successfully [15,32,34,38,39,65]. Outside of these main areas, it is possible to find other methodologies that use the idea of consensus in different ways to the aforementioned ones, with [41,46] being representative examples of these methodologies.…”