Abstract
Opinion dynamics are investigated extensively to describe the process of opinion formation in groups of individuals. Most of the existing opinion dynamics models assume that the individuals express numerical opinions. However, this assumption does not consider the fact that people often express their opinions in a linguistic way. Particularly, for linguistic opinions, an important point to be highlighted in computing with words (CW) is that words mean different things to different people. In this article, following the idea of personalized individual semantics (PIS) model, we propose the PIS-based linguistic opinions dynamics model (PIS-LOD model) in the framework of bounded confidence effects. Then, some desired properties in the PIS-LOD model are discussed in detail. Furthermore, we design the detailed simulation experiments to show that the individuals' familiarity (which refers to the knowledge on others' semantics) and the PISs' differences have a great influence on the stabilized time, distributions of the extreme and moderate opinions, linguistic opinions distribution, semantics distribution, number of clusters, consensus reaching and the extremely small clusters. The results in this article are very helpful for us to understand the process of group opinion formation in a linguistic context.