Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 165-171.doi: 10.11896/jsjkx.210400238

• Intelligent Computing • Previous Articles     Next Articles

TS-AC-EWM Online Product Ranking Method Based on Multi-level Emotion and Topic Information

YU Ben-gong1,2, ZHANG Zi-wei1, WANG Hui-ling1   

  1. 1 School of Management,Hefei University of Technology,Hefei 230009,China
    2 Key Laboratory of Process Optimization & Intelligent Decision-making,Ministry of Education,Hefei University of Technology,Hefei 230009,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:YU Ben-gong,born in 1971,Ph.D,professor.His main research interests include information systems and machine learning.
  • Supported by:
    National Natural Science Foundation of China(71671057).

Abstract: The information of e-commerce platforms has a significant impact on consumers' purchase decisions.It is of great research value to integrate the information of large-scale stores,commodity information and online review information and get online commodity ranking to assist consumers in purchasing decisions.To solve the problems,this paper proposes an online product ranking method TS-AC-EWM,which integrates multi-level emotion and topic information,and makes full use of scoring information and review content information.Firstly,the online commodity ranking evaluation system is designed from two dimensions of measurement and content,including four measurement indexes and three content indexes.Secondly,we crawl the measurement indexes and online review content of each candidate commodity.Thirdly,three content indexes are calculated by TS method,which combines topic and affective information,and AC method,which is based on appending comments.Finally,using the entropy weight method to calculate the index weight,commodity grading and sorting.Experiments on Jingdong microwave oven dataset prove the feasibility and effectiveness of the proposed method,so the ranking method has a practical significance.

Key words: ALBERT, Entropy weight method, LDA, Product ranking, TextCNN

CLC Number: 

  • TP391
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