Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240400070-9.doi: 10.11896/jsjkx.240400070

• Intelligent Computing • Previous Articles     Next Articles

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

SUI Haoran1, ZHOU Xiaohang2,3, ZHANG Ning1   

  1. 1 School of Business,Qingdao University,Qingdao,Shandong 266000,China
    2 School of Management,Qingdao City University,Qingdao,Shandong 266000,China
    3 School of Information Management and Engineering,Shanghai University of Finance and Economics,Shanghai 200000,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:SUI Haoran,born in 1999,postgra-duate,is a member of CCF(No.T8983G).Her main research interests include product innovation and business data analysis.
    ZHANG Ning,born in 1980,Ph.D,professor.His main research interests include product innovation and business data analysis.
  • Supported by:
    Natural Science Foundation of Shandong Province,China(ZR2022MG022,ZR2023MG076).

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Abstract: 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.

Key words: User-generated content, Product improvement, Attribute extraction, Attribute sentiment classification, Machine lear-ning, Deep learning

CLC Number: 

  • TP391
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