期刊文献+

基于书写行为与语义特征的评论有用性评估 被引量:2

Method of assessing Chinese online reviews based on writing behavior and semantic features
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摘要 针对电子商务网站充斥着大量有用性较低的评论,提出一种基于用户书写行为与语义特征的中文评论有用性评估方法。方法通过在Web客户端实时监听评论文本框值的变化,识别出句尾插入、非句尾插入、句尾删除、非句尾删除等书写行为,在服务器端根据书写行为、评论的语义特征建立的线性评估模型计算用户评论的有用性。实验结果表明该方法能够较为准确地识别有用性较高的评论。 At present, consumers are used to judging the quality of goods by online reviews, however, e-commerce sites are always filled with lots of less useful reviews. A method for assessing the helpfulness of Chinese online reviews based on writing behavior and semantic features is proposed in this paper. It recognizes the writing behavior such as tail-insertion,non-tail-insertion, selected-modification by real-time monitoring the comment text box value change in Web client, and then according to the linear weighted model established on writing behavior and semantic features of reviews, it assesses the helpfulness in server. The experimental results show that the model can accurately and efficiently recognize the useful reviews.
出处 《计算机工程与应用》 CSCD 北大核心 2016年第6期117-121,126,共6页 Computer Engineering and Applications
基金 国家自然科学基金(No.61471232)
关键词 评论有用性评估 书写行为 评论质量检测 评论语义特征 assessing the helpfulness of reviews writing behavior testing the quality of reviews semantic features of reviews
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参考文献15

  • 1David G,Dina M.Using online conversations to study wordof-mouth communication[J].Marketing Science,2004,23(4):545-560.
  • 2Cone公司.2011网络趋势影响跟踪[EB/OL].[2015-04-11].http://www.conecomm.com/contentmgr/showdetails.php/id/4008.
  • 3第31次中国互联网络发展状况统计报告[R].中国互联网络信息中心.2012:12.
  • 4林煜明,王晓玲,朱涛,周傲英.用户评论的质量检测与控制研究综述[J].软件学报,2014,25(3):506-527. 被引量:47
  • 5Kim S M,Pantel P,Chklovski T.Auto-matically assessing review helpfulness[C]//Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing,Sydney,Australia,2006.
  • 6Liu J J,Cao Y B,Lin C Y,et al.Low-quality product review detection in opinion summarization[C]//Proc of the2007 Joint Conf on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.Stroudsburg:Association for Computational Linguistics,2007:334-342.
  • 7Liu Y,Huang X J,An A J,et al.Modeling and predicting the helpfulness of online reviews[C]//Proc of the 8th IEEE Int’l Conf on Data Mining,Washington,USA,2008:443-452.
  • 8Lu Y,Tsaparas P,Ntoulas A,et al.Exploiting social context for review quality prediction[C]//Proc of the 19th Int’l Conf on World Wide Web,New York,USA,2010:691-700.
  • 9Tsur O,Rappoport A.A fully unsupervised algorithm for selecting the most helpful book reviews[C]//Proc of the3rd Int’l Conf on Weblogs and Social Media.Palo Alto:AAAI Press,2009:154-161.
  • 10姜巍,张莉,戴翼,蒋竞,王刚.面向用户需求获取的在线评论有用性分析[J].计算机学报,2013,36(1):119-131. 被引量:56

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