摘要
消费者满意度水平对企业生产运营至关重要,基于在线评论词向量,构建一种面向消费者满意度研究的分析框架SAFCS.基于BERT词向量,根据对比注意力从在线评论中选择重要名词,并利用UMAP-PCA法进行降维,聚类后获得相应领域消费者满意度维度;通过依存句法分析获得在线评论中的属性-观点短语,采用预训练语言模型实现属性-观点短语情感分类.采用AT,GRN,LN,TB 4个服装品牌的评论数据进行实验分析,结果表明,消费者对各维度关注度具有明显特点,同时,相较于消极评论,消费者在发表积极评论时更倾向于进行综合性评价.最后结合研究结果,为各品牌的生产运营策略选择提出相应建议.
Consumer satisfaction is essential for the production and operation of enterprises.In this regard,a framework SAFCS is proposed using word vectors derived from online reviews.Important nouns were selected from online reviews using contrastive 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 analysis 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 approaches for the brands.
作者
樊宇
任敏慧
张健
FAN Yu;REN Minhui;ZHANG Jian(School of Economics&Management,Beijing Information Science and Technology University,Beijing 100192;Beijing Key Laboratory of Big Data Decision Making for Green Development,Beijing 100192;Beijing International Science and Technology Cooperation Base for Intelligent Decision and Big Data Application,Beijing 100192)
出处
《系统科学与数学》
CSCD
北大核心
2024年第6期1570-1585,共16页
Journal of Systems Science and Mathematical Sciences
基金
国家重点研发计划课题(2019YFB1405303)
北京市属高等学校优秀青年人才培育计划项目(BPHR202203233)
国家自然科学基金面上项目(72174018)
教委科技一般项目“工业4.0背景下质量标准知识图谱构建与应用研究”资助课题
