基于混合神经网络的中文在线评论产品特征提取及消费者需求分析  

Extracting Product Features and Analyzing Customer Needs from Chinese Online Reviews with Hybrid Neural Network

作　　者：史丽丽 林军[1,2] 朱桂阳[3] Shi Lili;Lin Jun;Zhu;Guiyang(School of Management,Xi’an Jiaotong University,Xi’an 710049,China;The Key Lab of the Ministry of Education for Process Management&Efficiency Engineering,Xi’an 710049,China;School of Management,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]西安交通大学管理学院,西安710049 [2]过程管理与效率工程教育部重点实验室,西安710049 [3]杭州电子科技大学管理学院,杭州310018

出　　处：《数据分析与知识发现》2023年第10期63-73,共11页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金面上项目(项目编号:72071154,71672140)的研究成果之一。

摘　　要：【目的】从中文在线评论中提取产品特征,并结合评论内容对消费者需求进行分析。【方法】首先提出一种混合神经网络(HNN)模型用于从中文在线评论中提取产品特征,进一步将关键事件技术及抱怨和赞扬分析理论应用到Kano模型中,对产品特征进行分类和优先级排序。【结果】HNN模型的F1值达到94.85%,比变体基准模型平均提高10.52个百分点,比业界其他模型平均提高9.47个百分点。【局限】所提方法是一种监督方法,对标记信息的需求限制了其应用。【结论】所提方法通过解决中文产品特征提取的问题,提升了产品特征提取的精度。结合提取的特征进行消费者需求分析,对产品特征进行分类和优先级排序,为产品管理者构建产品提升策略奠定基础。[Objective]This study aims to extract product features and analyze customer needs based on the content of Chinese online reviews.[Methods]First,we proposed a hybrid neural network(HNN)to extract product features.Then,we applied critical incident technique(CIT)and analysis of complaints and compliments(ACC)to the Kano model to classify and prioritize product features.[Results]The F1 value of the HNN model reached 94.85%,which was 10.52 percentage points higher than the variant benchmark models and 9.47 percentage points over other leading models on average.[Limitations]The proposed model is supervised learning,and the need for labeling information restricts its application.[Conclusions]The proposed method improves the accuracy of product feature extraction,as well as classifies and prioritizes product features based on customer needs.It lays a foundation for managers to develop product improvement strategies.

关 键 词：中文在线评论 产品特征提取 消费者需求分析 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] F274[自动化与计算机技术—计算机科学与技术]