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现代图书情报技术  2014, Vol. 30 Issue (4): 65-70     https://doi.org/10.11925/infotech.1003-3513.2014.04.10
  情报分析与研究 本期目录 | 过刊浏览 | 高级检索 |
面向供应链的产品评论中客户关注特征挖掘方法研究
郝玫, 王道平
北京科技大学东凌经济管理学院 北京 100083
Mining Customer Focus Features from Product Reviews Oriented Supply Chain
Hao Mei, Wang Daoping
Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
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摘要 

[目的] 针对电子商务平台的中文产品评论,提出一种面向供应链的客户关注特征挖掘方法。[方法] 以产品评论数据预处理方法为核心,改进关联规则挖掘产品特征方法。预处理技术包括产品评价概念树、产品评价特征库和MA_Apriori算法。数据实验以京东商城平板电脑为例,在Weka环境中完成客户关注特征的挖掘。[结果] 实验表明,对于相同的事务文件,采用数据预处理再进行关联规则的产品特征挖掘,特征查全率为90.5%,而关联规则挖掘方法查全率仅为71.4%。并且本方法可实现产品特征挖掘结果的层次化和规范化。[局限] 需要进一步补充汉语分词系统的用户词典,添加产品领域相关的专业词汇,以提高分词准确性。[结论] 本方法有助于供应链各节点企业灵活选择产品评价概念层次,从而有针对性地实施产品改进和服务提升。

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郝玫
王道平
关键词 产品评价概念树客户关注特征关联规则数据挖掘供应链    
Abstract

[Objective] This paper proposes a customer focus feature mining method oriented supply chain. [Methods] The association rule mining is improved by adding data preprocessing, which includes product evaluation conception tree, product evaluation feature database and MA_Apriori algorithm. Based on the data of tablet PC of Jingdong Mall, the data experiment mines the customer focus features in Weka. [Results] The experiments show that the recall radio of new method is 90.5%, but the association rule method is 71.4%. In addition, it can get the hierarchical and standardized products features. [Limitations] Considering the accuracy of word segmentation, the user dictionary of segmentation system needs to be replenished by adding the product professional vocabulary. [Conclusions] This paper can help each enterprise select the product evaluation conception hierarchies flexibly, then improve the qualities of products and service.

Key wordsProduct evaluation conception tree    Customer focus feature    Association rule    Data mining    Supply chain
收稿日期: 2013-08-28      出版日期: 2014-05-19
:  TP391  
基金资助:

本文系国家自然科学基金项目“敏捷供应链知识服务网络形成、演化与治理机制研究”(项目编号:71172169)的研究成果之一。

通讯作者: 郝玫 E-mail:haomei@manage.ustb.edu.cn     E-mail: haomei@manage.ustb.edu.cn
作者简介: 作者贡献声明:郝玫:提出研究命题,设计研究方法,采集数据和进行实验,论文起草;王道平:分析数据和最终版本修订。
引用本文:   
郝玫, 王道平. 面向供应链的产品评论中客户关注特征挖掘方法研究[J]. 现代图书情报技术, 2014, 30(4): 65-70.
Hao Mei, Wang Daoping. Mining Customer Focus Features from Product Reviews Oriented Supply Chain. New Technology of Library and Information Service, 2014, 30(4): 65-70.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2014.04.10      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2014/V30/I4/65

[1] McKinsey.2011年度中国消费者调查报告[R].2011:22-34.(McKinsey.Chinese Consumer Survey Report for 2011[R].2011:22-34.)
[2] Popescu A M,Etzioni O.Extracting Product Features and Opinions from Reviews[C].In:Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing.Stroudsburg,PA,USA:Association for Computational Linguistics,2005:339-346.
[3] Kobayashi N,Inui K,Matsumoto Y,et al.Collecting Evaluative Expressions for Opinion Extraction[C].In:Proceedings of the 1st International Joint Conference on Natural Language Processing.2004:596-605.
[4] 姚天昉,程希文,徐飞玉,等.文本意见挖掘综述[J].中文信息学报,2008,22(3):71-80.(Yao Tianfang,Cheng Xiwen,Xu Feiyu,et al.A Survey of Opinion Mining for Texts[J].Journal of Chinese Information Processing,2008,22(3):71-80.)
[5] Zhuang L,Jing F,Zhu X Y.Movie Review Mining and Summarization[C].In:Proceedings of the 15th ACM International Conference on Information and Knowledge Management.New York:ACM,2006:43-50.
[6] 伍星,何中市,黄永文.产品评论挖掘研究综述[J].计算机工程与应用,2008,44(36):37-40.(Wu Xing,He Zhongshi,Huang Yongwen.Product Review Mining:A Survey[J].Computer Engineering and Applications,2008,44(36):37-40.)
[7] Hu M,Liu B.Mining Opinion Features in Customer Reviews[C].In:Proceedings of the 19th National Conference on Artificial Intelligence.AAAI Press,2004:755-760.
[8] Agrawal R,Srikant R.Fast Algorithms for Mining Association Rules in Large Databases[C].In:Proceeding of the 20th International Conference on Very Large Data Bases,Santiago de Chile.1994:487-499.
[9] Liu B,Hsu W,Ma Y.Integrating Classification and Association Rule Mining[C].In:Proceedings of the KDD-98.1998:80-86.
[10] Popescu A M,Etzioni O.Extracting Product Features and Opinions from Reviews[C].In:Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing,Vancouver,Canada.2005:339-346.
[11] 李实,叶强,李一军,等.中文网络客户评论的产品特征挖掘方法研究[J].管理科学学报,2009,12(2):142-151.(Li Shi,Ye Qiang,Li Yijun,et al.Mining Features of Products from Chinese Customer Online Reviews[J].Journal of Management Sciences in China,2009,12(2):142-151.)
[12] Wang B,Wang H.Bootstrapping both Product Properties and Opinion Words from Chinese Reviews with Cross-training[C].In:Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence.Washington,DC,USA:IEEE Computer Society,2007:259-262.
[13] Zheng Y,Ye L,Wu G,et al.Extracting Product Features from Chinese Customer Reviews[C].In:Proceedings of the 3rd International Conference on Intelligent System and Knowledge Engineering.Washington:IEEE Computer Society,2008:285-290.
[14] 肖娟,叶枫.基于概念层次树的数据挖掘算法及应用研究[J].计算机应用研究,2005,22(3):61-64.(Xiao Juan,Ye Feng.Research on Data Mining Algorithm Based on Conception Hierarchy Tree and Its Application[J].Application Research of Computers,2005,22(3):61-64.)
[15] 王圣广,马士华.论供应链驱动模式[J].中国软科学,1999(4):34-36.(Wang Shengguang,Ma Shihua.Research on Supply Chain Driven Mode[J].China Soft Science,1999(4):34-36.)
[16] Maedche A,Motik B,Stojanovic L,et al.An Infrastructure for Searching,Reusing and Evolving Distributed Ontologies[C].In:Proceedings of the 12th International Conference on World Wide Web.2003:439-448.
[17] 周立柱,林玲.聚焦爬虫技术研究综述[J].计算机应用,2005,25(9):1965-1969.(Zhou Lizhu,Lin Ling.Survey on the Research of Focused Crawling Technique[J].Computer Applications,2005,25(9):1965-1969.)
[18] Hu M,Liu B.Mining and Summarizing Customer Reviews[C].In:Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Seattle,USA.2004:168-177.

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