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基于文本挖掘的不同购物网站商品评论一致性研究 被引量:6

Text Mining- based Consistency of Product Reviews in Different Shopping Websites
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摘要 基于文本挖掘的理论,提出不同购物网站商品评论对比分析的方法,对不同购物网站同一商品评论是否一致进行研究。首先对商品单个特征的评论进行对比分析,然后衍生到商品的整体特征对比。研究发现,不同购物网站对同一商品的评论并不完全一致,这种不一致主要体现在商品特征上面,这说明商品评论会因为购物网站的不同而有所差异。 Based on the theory of text mining, this paper puts forward a contrast method of product reviews in different shopping websites, and makes analysis on whether the product reviews from different shopping websites are consistent. Firstly, this paper analyses the reviews of product feature one by one. Then, it makes contrast analysis from one product feature to total product features. The study discovers that the reviews of the same product from different shopping websites are not completely consistent, and this inconsistency mainly reflects in product features, which means product reviews will be different due to different shopping websites.
机构地区 河海大学商学院
出处 《现代图书情报技术》 CSSCI 北大核心 2011年第12期64-68,共5页 New Technology of Library and Information Service
基金 教育部人文社会科学基金项目"组织间关系对软件外包联盟拓展市场能力的影响研究"(项目编号:10yjc630085) 江苏省教育厅哲学社会科学基金重点项目"企业国际化发展中技术平台战略选择研究"(项目编号:09SJD630003) "211工程"三期重点学科建设项目(技术经济与管理)的研究成果之一
关键词 商品评论 购物网站 文本挖掘 Product reviews Shopping websites Text mining
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参考文献14

  • 1孔亮 石磊 孙伯等.Web评论主流观点提取及不同源数据的对比分析.计算机研究与发展,2009,:1-7.
  • 2Li N, Wu D D. Using Text Mining and Sentiment Analysis for On- line Forums Hotspot Detection and Forecast [ J ]. Decision Support Systems,2010,48(2) :354-368.
  • 3Tsai F S, Kwee A T. Database Optimization for Novelty Mining of Business Blogs [ J ]. Expert Systems with Applications ,2011,38 (9) : 11040 -11047.
  • 4Chang C W, Lin C T, Wang L Q. Mining the Text Information to Optimizing the Customer Relationship Management [ J]. Expert Sys- tems with Applications ,2009,36 (2) : 1433 - 1443.
  • 5Drewes B. Some Industrial Applications of Text Mining [ J ]. Stud- Fuzz,2005,185:233 - 238.
  • 6Roussinov D, Zhao J L. Automatic Discovery of Similarity Relation- ships Through Web Mining [ J ]. Decision Support Systems, 2003,35 (1) : 149 -166.
  • 7Thorleuchter D, Poel D V d, Prinzie A. Mining Ideas from Textual Information[ J ]. Expert Systems with Applications, 2010,37 ( 10 ) : 7182 -7188.
  • 8Turney P, Littman M. Measuring Praise and Criticism: Inference of Semantic Orientation from Association [ J ]. ACM Transactions on Information Systems, 2003,21 (4) :315 - 346.
  • 9Liu B, Hu M, Cheng J. Opinion Observer: Analyzing and Compa- ring Opinions on the Web [ C ]. In: Proceedings of the 14th Interna- tional World Wide Web Conference. Now York: ACM Press,2005: 324 - 351.
  • 10王琦,唐世渭,杨冬青,王腾蛟.基于DOM的网页主题信息自动提取[J].计算机研究与发展,2004,41(10):1786-1792. 被引量:81

二级参考文献19

  • 1O Buyukkokten, H Garcia-Molina, A Paepcke. Accordion summarization for end-game browsing on PDAs and cellular phones. In: Proc of ACM Conf on Human Factors in Computing Systems(CHI 2001). New York: ACM Press, 2001. 213~220
  • 2Wang Tengjiao, Tang Shiwei, Yang Dongqing, et al. COMIIX:Towards effective WEB information extraction, integration and query answering. In: Proc of SIGMOD' 02. New York: ACM Press, 2002. 620
  • 3Liu Ling, Pu Calton, Han Wei. XWRAP: An XML-enabled wrapper construction system for Web information sources. In:Proc of the 16th Int'l Conf on Data Engineering. Washington:IEEE Computer Society Press, 2000. 611~621
  • 4R Baumgartner, S Flesca, G Gottlob. Visual Web information extraction with Lixto. In: Proc of the 27th Int'l Conf on Very Large Data Bases. San Francisco: Morgan Kaufmann, 2001. 119~ 128
  • 5D Freitag. Machine learning for information extraction in information domains. Machine Learning, 2000, 39 (2-3): 169 ~202
  • 6S SoderLan. Learning information extraction rules for semistructured and free text. Machine Learning, 1999, 34(1-3): 233~ 272
  • 7R D Doorenbos, O Etzioni, D S Weld. A scalable comparasonshopping agent for the World-Wide Web. In: ACM Agents' 97.New York: ACM Press, 1997. 39~48
  • 8D W Embley, et al. Conceptual-model-based data extraction from multiple-record Web pages. Data and Knowledge Engineering,1999, 31(3): 227~251
  • 9A Finn, A Kushmerick, B Smyth. Fact or fiction: Content classification for digital libraries. The 2nd DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries, Dublin, Ireland, 2001
  • 10S Gupta, G Kaiser, D Neistadt, et al. DOM-based content extraction of HTML documents. In: Proc of the 12th Int'l World-Wide Web Conf. New York: ACM Press, 2003. 207~214

共引文献144

同被引文献64

  • 1倪茂树,林鸿飞.基于关联规则和极性分析的商品评论挖掘[C]//第三届全国信息检索与内容安全学术会议,2007:635-642.
  • 2孔亮 石磊 孙伯等.Web评论主流观点提取及不同源数据的对比分析.计算机研究与发展,2009,:1-7.
  • 3姚天昉,娄德成.汉语语句主题语义倾向分析方法的研究[J].中文信息学报,2007,21(5):73-79. 被引量:78
  • 4Jumin Lee,Do-Hyung Park,Ingoo Han.The effect of negative online consumer reviews on product attitude: An information processing view[J]Electronic Commerce Research and Applications,2007(3).
  • 5Pang B, Lee L, Vaithyanathan S. Thumb up? Sentiment classification using machine learning techniques[C]// Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing(EMNLP). 2002:79-86.
  • 6Turney P D. Thumbs up or Thumbs down? Semantic orientation applied to unsupervised classification of reviews[C]// Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics(ACL). 2002:417-424.
  • 7Liu Bing. Sentiment analysis and subjectivity [M]// Handbook of Natural Language Processing(Second Edition). 2010:Chapter 28.
  • 8宇缨. Web文本观点挖掘综述[J]. 计算机科学, 2010,37(10A):122-126.
  • 9Pang B, Lee L. Opinion mining and sentiment analysis[J]. Foundations and Trends in Information Retrieval, 2008,2(1-2):1-135.
  • 10Wiebe J, Bruce R F, O’Hara T P. Development and use of a gold standard data set for subjectivity classifications[C]// Proceedings of the Association for Computational Linguistics(ACL). 1999:246-253.

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