基于细粒度观点挖掘和Kano模型的用户满意度分析研究

计算机工程与科学 ›› 2023, Vol. 45 ›› Issue (04): 701-710.

基于细粒度观点挖掘和Kano模型的用户满意度分析研究

曾祥俊1,叶晓庆2,刘盾1

(1.西南交通大学经济管理学院,四川成都 610031;2.西南交通大学计算机与人工智能学院,四川成都 611756)

收稿日期:2022-09-08 修回日期:2022-10-21 接受日期:2023-04-25 出版日期:2023-04-25 发布日期:2023-04-13
基金资助:
国家自然科学基金(62276217,61876157);四川省杰出青年科学基金(2022JDJQ0034);重庆市计算智能重点实验室项目(2020FF03);西南交通大学杨华学者A类计划(201806)

Customer satisfaction analysis based on fine-grained opinion mining and Kano model

ZENG Xiang-jun1,YE Xiao-qing2,LIU Dun1

(1.School of Economics and Management,Southwest Jiaotong University,Chengdu 610031;
2.School of Computer and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China)

Received:2022-09-08 Revised:2022-10-21 Accepted:2023-04-25 Online:2023-04-25 Published:2023-04-13

摘要/Abstract

摘要： 在线评论在客户关系管理、产品营销等方面发挥着重要作用，有效利用在线评论来分析用户满意度，对企业改善其服务和产品至关重要。传统的满意度分析方法的变量设计往往依赖专家建议，较少考虑正负属性的不对称影响。为解决这些问题，利用文本细粒度观点挖掘技术，对用户在线评论进行特征挖掘，构建产品服务质量分数，并采用PRCA技术对服务属性的正负影响进行量化，将服务属性投射为Kano属性分类，然后分析不同粒度下不同品牌的客户满意度特点，并给出不同品牌的属性优先顺序。最后，从咖啡评论数据中挖掘出5个关键属性。实验结果表明，不同属性对满意度影响具有不对称效应，且不同粒度下的顾客满意度影响因素具有不同的特点，并给出了相应的精细化企业管理策略。

关键词: 在线评论, 细粒度观点挖掘, Kano模型, 满意度分析, 奖惩对比分析

Abstract: Online reviews play an important role in customer relationship management, product marketing and other aspects. Effectively using online reviews to analyze user satisfaction is crucial for enterprises to improve their services and products. The variable design of traditional satisfaction analysis methods often relies on expert advice and seldom considers the asymmetric influence of positive and negative attributes. To solve these problems, this paper utilizes opinion mining technology to explore the features of customers online reviews and calculate services quality scores. Besides, PRCA technology is adopted to quantify the positive and negative influences of service attributes, and classify service attributes to Kano categories. Then, the characteristics of the different brand customer satisfaction under different granularity are analyzed, and the priority order of different customers' attributes is given. Finally, this paper mines five common attributes from coffee reviews. The experimental results show that different attributes have asymmetric effects on satisfaction and the influencing factors of customer satisfaction under different granularity have different characteristics. The corresponding refined enterprise management strategy is given.

Key words: online review, fine-grained opinion mining, Kano model, satisfaction analysis, penalty- reward contrast analysis

曾祥俊, 叶晓庆, 刘盾. 基于细粒度观点挖掘和Kano模型的用户满意度分析研究[J]. 计算机工程与科学, 2023, 45(04): 701-710.

ZENG Xiang-jun, YE Xiao-qing, LIU Dun. Customer satisfaction analysis based on fine-grained opinion mining and Kano model[J]. Computer Engineering & Science, 2023, 45(04): 701-710.