Mining emotion soft factors in linguistic preference time sequences based on personalized individual semantics in group decision-making

Abstract

Individuals’ emotions, such as hesitation and unwavering confidence, can influence the ability of decision-makers (DMs) to make rational judgments. The emotion is always hidden in individual preference series, which is referred to as emotion soft factors, It is a prerequisite for avoiding unfavorable impacts on consensus reaching process. This study focuses on structuring a consensus model with emotion soft factors in linguistic preference time sequence. Specifically, a personalized individual semantics (PIS) learning process is implemented to obtain the personalized numerical scales of DMs’ linguistic terms. Subsequently, we propose a consensus model incorporating the consensus measurement and feedback modification phase. In the process, a grey clustering scheme is devised to mine emotion soft factors from DMs’ preference sequences and manage individuals in different grey classes. Finally, numerical examples, simulation analysis, and comparison study are presented to illustrate the influence of different parameters and justify the validity of the proposed model.