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基于用户行为及文本内容的垃圾评论识别研究 被引量：1

Research on Review Spam Recognition Based on User Behavior and Text Content
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摘要 从海量的在线评论中自动识别出垃圾评论,是消费者及厂家有效利用在线评论的前提。结合垃圾评论者的行为特征及评论文本内容,采用支持向量机构建了垃圾评论识别模型。实验结果表明,该方法有效地识别了垃圾评论。 Automated recognition of review spam from massive online reviews is a prerequisite for the effective use of online reviews by consumers and manufacturers.Based on the behavior characteristics of the commentator and the content of the comment text,this paper uses the support vector machine to construct the spam evaluation model.The experimental results show that this method can effectively identify the spam.
作者 胡龙茂
机构地区 安徽财贸职业学院
出处 《重庆科技学院学报（自然科学版）》 CAS 2017年第5期105-107,共3页 Journal of Chongqing University of Science and Technology：Natural Sciences Edition
基金 安徽省高校自然科学研究重点项目"基于产品评论细粒度情感分析的消费者偏好模型构建研究"(KJ2017A858)
关键词 垃圾评论 行为特征 评论文本 review spam behavior characteristics review text
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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  • 1Becchetti L, Castillo C. Donato D, et al. Link Analysis for Web Spam Detection[EB/OL]. (2010-11-21). http://www.chato.cl/ papers/becchetti_2007 link analysis web_spam_detection.pdf.
  • 2Chirita P A, Diederich J, Nejdl W. MailRank: Using Ranking for Spam Detection[C]//Proc. of the 14th ACM International Con- ference on Information and Knowledge Management. New York, USA: [s. n.], 2005.
  • 3Benevenuto F, Magno G, Rodrigues T, et al. Detecting Spammers on Twitter[C]//Proc. of Anti-abuse and Spam Conference on Collaboration, Electronic Messaging. [S. 1.]: IEEE Press, 2010.
  • 4Gilbert E, Karahalios K. Understanding Deja Reviewers[C]//Proc. of ACM Conference on Computer Supported Cooperative Work. New York, USA: [s. n.], 2010.
  • 5Dellarocas C. Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behavior[C]//Proc. of the 2nd ACM Conference on Electronic Commerce. New York, USA: [s. n.], 2000.
  • 6Wu Guangyu, Greene D, Smyth B, et al. Distortion As a Validation Criterion in the Identification of Suspicious Reviews[EB/OL]. (2010-11-21). http://www.csi.ucd.ie/content/distortion-validation- criterion-identification-suspicious-reviews.
  • 7Mizil C D N, Kossinets G, Kleinberg J, et al. How Opinions are Received by Online Communities: A Case Study on Amazon.corn Helpfulness Votes[C]//Proc. of the 18th International Conference on World Wide Web. New York, USA: [s. n.], 2009.
  • 8Lim E P, Nguyen V A, Jindal N, et al. Detecting Product Review Spammers Using Rating Behaviors[C]//Proc. of the 19th ACM International Conference on Information and Knowledge Management. New York, USA: [s. n.], 2010.
  • 9JAarvelin K, KekAalAainen J. 1R Evaluation Methods for Retrieving Highly Relevant Documents[C]//Proc. of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: [s. n.], 2000.
  • 10Bos W, Tarnai C. Content Analysis in Empirical Social Research[J]. International Journal of Educational Research, 1999, 31(8): 659- 671.

共引文献24

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重庆科技学院学报（自然科学版）
2017年 第5期

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