计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 165-171.doi: 10.11896/jsjkx.210400238

• 智能计算 • 上一篇    下一篇

一种融合多层次情感和主题信息的TS-AC-EWM在线商品排序方法

余本功1,2, 张子薇1, 王惠灵1   

  1. 1 合肥工业大学管理学院 合肥 230009
    2 合肥工业大学过程优化与智能决策教育部重点实验室 合肥 230009
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 余本功(bgyu19@163.com)
  • 基金资助:
    国家自然科学基金(71671057)

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).

摘要: 电商平台信息对消费者的商品购买决策有显著影响。基于大体量的店铺与商品信息、在线评论文本进行信息融合并得出在线商品排序辅助消费者进行购买决策,具有重要的研究价值。针对上述问题,提出了一种融合多层次情感和主题信息的TS-AC-EWM在线商品排序方法,充分利用了评分信息与评论内容信息。首先,从计量与内容两个维度设计在线商品排序评价体系,体系包含4个计量指标与3个内容指标;其次,爬取各候选商品的计量指标与在线评论内容;然后,用融合主题与情感信息的TS方法以及基于追加评论的AC方法计算3个内容指标;最后,用熵权法确定指标权重,得出商品评分及排序。以京东微波炉数据集为例进行实验,证明了所提方法的可行性与有效性,因此该排序方法具有一定的现实意义。

关键词: ALBERT, LDA, TextCNN, 商品排序, 熵权法

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

中图分类号: 

  • TP391
[1] E-COMMERCE IN CHINA 2019[R].Beijing:Department ofElectronic Commerce and Information,2020.
[2] CHEN H,CHIANG R H L,STOREY V C.Business intelli-gence and analytics:From big data to big impact[J].MIS Quarterly,2012,36(4):1165-1188.
[3] FAN Z P,LI G M,LIU Y.Processes and methods of information fusion for ranking products based on online reviews:An overview[J].Information Fusion,2020,60:87-97.
[4] FU X,OUYANG T,YANG Z,et al.A product ranking method combining the features-opinion pairs mining and interval-valued Pythagorean fuzzy sets[J].Applied Soft Computing,2020,97(3):106803.
[5] CHEN M M,WANG L,WANG F D.An Empirical Study on the Critical Determinants of Online Consumer Decision Making[J].Journal of Modern Information,2014,34(2):37-42.
[6] ZHOU M,CHEN A,KOU Z L.How do user reviews affect online marketplace purchases?[J].Consumer Economics,2018,34(3):72-79.
[7] LI Z W,ZHANG Y H,LUAN D Q.What Factors InfluenceConsumers' Online Purchasing Decisions?——Customer Perceived Value Drivers[J].Management Review,2017,29(8):136-146.
[8] ZHANG D P,CHEN C F,ZHANG F L.The Impact of Online Evaluation on Consumer Behavior:A Case Study of Off-sale O2O Platform[J].Enterprise Economics,2017,36(3):144-149.
[9] CHAU P Y K,HU P J H,LEE B L P,et al.Examining customers' trust in online vendors and their dropout decisions:An empirical study[J].Electronic Commerce Research and Applications,2007,6(2):171-182.
[10] SALEHAN M,KIM D J.Predicting the Performance of Online Consumer Reviews:A Sentiment Mining Approach to Big Data Analytics[J].Decision Support Systems,2016,81:30-40.
[11] HUANG A H,CHEN K,YEN D C,et al.A study of factorsthat contribute to online review helpfulness[J].Computers in Human Behavior,2015,48:17-27.
[12] LIU Z,PARK S.What makes a useful online review? Implication for travel product websites[J].Tourism Management,2015,47:140-151.
[13] LIU Y,BI J W,FAN Z P.Ranking products through online reviews:A method based on sentiment analysis technique and intuitionistic fuzzy set theory[J].Information Fusion,2017,36:149-161.
[14] XU Y,ZHANG H,CHEN L.A Fuzzy Comprehensive Evaluation Method for User Generated Content Based on Sentimental Analysis[J].Information Studies:Theory & Application,2016,39(6):64-69.
[15] CHEN K,KOU G,SHANG J,et al.Visualizing market structure through online product reviews:integrate topic modeling,TOPSIS,and multi-dimensional scaling approaches[J].Electronic Commerce Research and Applications,2015,14(1):58-74.
[16] YU B,ZHANG P,XU Q.Selecting Products Based on F-BiGRU Sentiment Analysis[J].Data Analysis and Knowledge Disco-very,2018,2(9):22-30.
[17] ZHANG J,YOU T.Method for Selecting Desirable Product(s) Through Multiple Attribute Online Reviews Considering Customer's Aspirations[J].Journal of Industrial Engineering and Engineering Management,2020,34(5):24-31.
[18] LIU Y,JIAN L.Data Mining of E-commerce Online Reviews Based on Sentiment Analysis[J].Journal of Statistics and Information Forum,2018,33(12):119-124.
[19] ZHENG K,ZHANG Z,SONG B.E-commerce logistics distribution mode in big-data context:A case analysis of JD.COM[J].Industrial Marketing Management,2020,86:154-162.
[20] HONG W,LI M.A review:Text sentiment analysis methods[J].Computer Engineering & Science,2019,41(4):750-757.
[21] GAO H,NA R S,YANG F.Sentiment Analysis of Online Reviews Based on Ensemble Learning[J].Information Science,2019,37(11):48-52,111.
[22] GUAN P F,LI B A,LV X Q,et al.Attention Enhanced Bi-directional LSTM for Sentiment Analysis[J].Journal of Chinese Information Processing,2019,33(2):105-111.
[23] DONG J,HE F,GUO Y,et al.A Commodity Review Sentiment Analysis Based on BERT-CNN Model[C]//2020 5th International Conference on Computer and Communication Systems(ICCCS).Shanghai:IEEE Press,2020:143-147.
[24] DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding[J].arXiv:1810.04805,2018.
[25] MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Proc. of NIPS.Cambridge:MIT Press,2013:3111-3119.
[26] MIKOLOV T,CHEN K,CORRADO G,et al.Efficient Estimation of Word Representations in Vector Space[J].arXiv:1301.3781,2013.
[27] LAN Z,CHEN M,GOODMAN S,et al.ALBERT:A Lite BERT for Self-supervised Learning of Language Representations[J].arXiv:1909.11942,2019.
[28] ZHU X,SONG J,ZHANG X,et al.Review of Text Emotion Analysis Based on Topic Mining Technology[J].Information Studies:Theory & Application,2019,42(11):156-163.
[29] BLEI D M,NG A Y,JORDAN M I.Latent Dirichlet Allocation[J].Journal of Machine Learning Research,2003,3:993-1022.
[30] LI C,YI H,WU H,et al.On Patent Technology Thematic Analy-sis of Driverless Cars:WI-LDA as Thematic Model[J].Journal of Intelligence,2018,37(12):50-55,42.
[31] LI C,WU H,YI H,et al.Research on Technology Layout of China-Japan-US Hydrogen Energy Industry Chain Based on Improved LDA Theme Model[J].Journal of Intelligence,2019,38(7):78-84,110.
[32] SHI W,GONG X,ZHANG Q,et al.A Comparative Study on the First-time Online Reviews and Appended Online Reviews[J].Journal of Management Science,2016,29(4):45-58.
[1] 王俊, 王修来, 庞威, 赵鸿飞.
面向科技前瞻预测的大数据治理研究
Research on Big Data Governance for Science and Technology Forecast
计算机科学, 2021, 48(9): 36-42. https://doi.org/10.11896/jsjkx.210500207
[2] 罗长银, 陈学斌, 马春地, 张淑芬.
基于层析分析改进的联邦平均算法
Improved Federated Average Algorithm Based on Tomographic Analysis
计算机科学, 2021, 48(8): 32-40. https://doi.org/10.11896/jsjkx.201000093
[3] 刘蕴涵, 沙朝锋, 牛军钰.
基于Stack Overflow的数据库相关主题分析
Analysis of Topics on Database Systems in Stack Overflow
计算机科学, 2021, 48(6): 48-56. https://doi.org/10.11896/jsjkx.200800217
[4] 史朝卫, 孟相如, 马志强, 韩晓阳.
拓扑综合评估与权值自适应的虚拟网络映射算法
Virtual Network Embedding Algorithm Based on Topology Comprehensive Evaluation and Weight Adaptation
计算机科学, 2020, 47(7): 236-242. https://doi.org/10.11896/jsjkx.190600022
[5] 周凯, 任怡, 汪哲, 管剑波, 张芳, 赵言亢.
基于主题模型的Ubuntu操作系统缺陷报告的分类及分析
Classification and Analysis of Ubuntu Bug Reports Based on Topic Model
计算机科学, 2020, 47(12): 35-41. https://doi.org/10.11896/jsjkx.200100022
[6] 王胜, 张仰森, 张雯, 蒋玉茹, 张睿.
基于SL-LDA的领域标签获取方法
Domain Label Acquisition Method Based on SL-LDA Model
计算机科学, 2020, 47(11): 95-100. https://doi.org/10.11896/jsjkx.190900012
[7] 王涵, 夏鸿斌.
LDA模型和列表排序混合的协同过滤推荐算法
Collaborative Filtering Recommendation Algorithm Mixing LDA Model and List-wise Model
计算机科学, 2019, 46(9): 216-222. https://doi.org/10.11896/j.issn.1002-137X.2019.09.032
[8] 张蕾,蔡明.
基于主题融合和关联规则挖掘的图像标注
Image Annotation Based on Topic Fusion and Frequent Patterns Mining
计算机科学, 2019, 46(7): 246-251. https://doi.org/10.11896/j.issn.1002-137X.2019.07.037
[9] 张小川, 余林峰, 张宜浩.
基于LDA的多特征融合的短文本相似度计算
Multi-feature Fusion for Short Text Similarity Calculation Based on LDA
计算机科学, 2018, 45(9): 266-270. https://doi.org/10.11896/j.issn.1002-137X.2018.09.044
[10] 邱先标, 陈笑蓉.
一种基于SA_LDA模型的文本相似度计算方法
Text Similarity Calculation Algorithm Based on SA_LDA Model
计算机科学, 2018, 45(6A): 106-109.
[11] 张景,朱国宾.
基于CBOW-LDA主题模型的Stack Overflow编程网站热点主题发现研究
Hot Topic Discovery Research of Stack Overflow Programming Website Based on CBOW-LDA Topic Model
计算机科学, 2018, 45(4): 208-214. https://doi.org/10.11896/j.issn.1002-137X.2018.04.035
[12] 罗海蛟,柯晓华.
基于改进的LDA模型的中文主观题自动评分研究
Automated Scoring Chinese Subjective Responses Based on Improved-LDA
计算机科学, 2017, 44(Z11): 102-105. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.020
[13] 杨玥,张德生.
中文文本的主题关键短语提取技术
Technology of Extracting Topical Keyphrases from Chinese Corpora
计算机科学, 2017, 44(Z11): 432-436. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.092
[14] 王振飞,刘凯莉,郑志蕴,王飞.
面向时间序列的微博话题演化模型研究
Research on Evolution Model of Microblog Topic Based on Time Sequence
计算机科学, 2017, 44(8): 270-273. https://doi.org/10.11896/j.issn.1002-137X.2017.08.046
[15] 庞雄文,万本帅,王盼.
基于MRT-LDA模型的微博文本分类
Micro-blog’s Text Classification Based on MRT-LDA
计算机科学, 2017, 44(8): 236-241. https://doi.org/10.11896/j.issn.1002-137X.2017.08.040
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!