基于UGC的产品改进:属性提取和属性情感分类的方法与应用综述

Product Improvement Based on UGC:Review on Methods and Applications of Attribute Extraction and Attribute Sentiment Classification

用户生成内容(User-generated Content,UGC)包含了大量用户对产品及其属性的真实看法。随着数字技术的持续发展,企业日益重视利用UGC来洞察用户需求,从而指导产品改进。在这一过程中,属性提取与属性情感分类被视为两大核心环节。属性提取旨在从UGC中识别出关键的产品属性,其方法可归为有监督学习和无监督学习两类;属性情感分类则用于分析用户对这些属性的情感态度,主要包括基于词典与规则、基于统计机器学习以及基于深度学习的方法。文中首先对属性提取与属性情感分类方法的理论框架及技术要点进行系统梳理,随后结合实际应用进行阐述,以期为利用UGC指导产品改进的企业和研究者提供有价值的参考。最终,探讨了当前属性提取与属性情感分类所面临的挑战及未来的研究方向。

User-generated content(UGC)contains a wealth of authentic user feedback on products and their attributes.With the continuous advancement of digital technology,enterprises are increasingly relying on UGC to gain insights into user needs and guide product improvements.In this process,attribute extraction and attribute sentiment classification are considered as two core steps.Attribute extraction aims to identify key product attributes from UGC and is mainly categorized into supervised and unsupervised learning methods.Attribute sentiment classification,meanwhile,focuses on analyzing users’emotional attitudes towards these extracted attributes,primarily including approaches based on dictionaries and rules,statistical machine learning,and deep learning.Firstly,systematically outlines the theoretical frameworks and technical essentials of attribute extraction and attribute sentiment classification methods.Subsequently,these methods are illustrated through practical applications,aiming to offer valuable references for enterprises and researchers utilizing UGC to inform product enhancements.Finally,this paper explores the current challenges faced by attribute extraction and sentiment classification,as well as directions for future research.