Consumer Satisfaction Research:An Analytical Framework Based on Word Vectors of Online Reviews
Consumer satisfaction is essential for the production and operation of en-terprises.In this regard,a framework SAFCS is proposed using word vectors derived from online reviews.Important nouns were selected from online reviews using con-trastive attention based on BERT word vectors.The UMAP-PCA method was used for dimension reduction,and consumer satisfaction dimensions corresponding to the domain were obtained after clustering.Attribute-opinion phrases from online reviews were acquired via dependency parsing,and a pre-trained language model was utilized to achieve sentiment classification of the attribute-opinion phrases.Empirical analy-sis was conducted using reviews from four clothing brands:AT,GRN,LN,and TB.The results indicate that consumers have obvious characteristics in their attention to various dimensions,and at the same time,compared to negative reviews,consumers tend to conduct comprehensive evaluations when posting positive reviews.Finally,the study results provided guidance on the preferred production and operational ap-proaches for the brands.
Online reviewsconsumer satisfactionpre-trained language modelsBERT word vectorsdependency parsing
NETL
NSTL
万方数据
