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.
作者
史丽丽
林军
朱桂阳
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)
出处
《数据分析与知识发现》
EI
CSCD
北大核心
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。
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
Chinese Online Reviews
Product Feature Extraction
Customer Requirements Analysis
Deep Learning
