Modeling Personalized Individual Semantics of New Energy Vehicle Consumers from User-Generated Content Considering Group Heterogeneity and Individual Consistency

In user-generated content (UGC) on online automotive platforms, consumers typically express their evaluations in the form of numerical ratings and textual reviews. To gain deeper insights into consumer preferences regarding new energy vehicles, it is essential to extract personalized individual semantics from UGC. Here, this study proposed a novel method motivated by UGC characteristics for personalized individual semantic (PIS) analysis. The developed method considers both group heterogeneity and individual consistency between the numerical and linguistic evaluations of vehicles in terms of different criteria. Based on the linguistic distribution assessments converted from UGC, we used k-nearest neighbor clustering to aggregate group opinions. Then, two optimization models were constructed based on maximum group heterogeneity and minimum information deviation to model the PISs of these groups. A comprehensive optimization model was also established to assign PISs to flexibly manage various scenarios. To demonstrate the effectiveness and applicability of the proposed model, this study conducted a case study and comparative analysis with evidence from the Pacific Automotive website ( www.pcauto.com.cn ). The results indicated that the proposed method can effectively reveal the personalized semantic preferences of individual and group buyers, with reference value for potential consumers and enterprises.