Research on Product Review Attribute-Based of Emotion Evaluate Review Spam Detection
Chen Yanfang1,2, Li Zhiyu1,2,3
1. School of Information Management, Central China Normal University, Wuhan 430079, China;
2. E-Commerce Research Center of Hubei Province, Central China Normal University, Wuhan 430079, China;
3. School of Information, Renmin University of China, Beijing 100872, China
[Objective] A model of Review Attribute of Product-Based Emotion Evaluate(RAPBEE) Model is proposed to detect fake reviews of online products. [Methods] Combined with the known research on the reviews effectiveness evaluation, the measuring method of review attribute of product-based emotion outlier detection is used to comprehensive sort the reliability of the reviews, so as to detect the fake reviews. [Results] The test data set is based on the R language to run the model, the results show that after calculated by the RAPBEE model the review sequencing has achieved 86.2% of agreement compared with the real situation which indicates that the RAPBEE model has a strong practical ability and fitness. [Limitations] The model stability depends on the modeling way of the attribute dictionary and the method also can be improved when dealing with large amounts of data set(Big Data). [Conclusions] The paper proposes a new method to deal with the Chinese fake reviews detection of online products, and this method has a strong expandability in reality.
陈燕方, 李志宇. 基于评论产品属性情感倾向评估的虚假评论识别研究[J]. 现代图书情报技术, 2014, 30(9): 81-90.
Chen Yanfang, Li Zhiyu. Research on Product Review Attribute-Based of Emotion Evaluate Review Spam Detection. New Technology of Library and Information Service, 2014, 30(9): 81-90.
[1] Jindal N, Liu B. Analyzing and Detecting Review Spam[C]. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM'07). Washington, D C: IEEE Computer Society, 2007: 547-552.
[2] Jindal N, Liu B. Review Spam Detection [C]. In: Proceedings of the 16th International Conference on World Wide Web (WWW'07). New York: ACM, 2007: 1189-1190.
[3] Jindal N, Liu B. Opinion Spam and Analysis [C]. In: Proceedings of the 2008 International Conference on Web Search and Data Mining (WSDM'08). New York: ACM, 2008: 219-230.
[4] Jindal N, Liu B, Lim E. Finding Unusual Review Patterns Using Unexpected Rules [C]. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM'10). New York: ACM, 2010: 1549-1552.
[5] 林煜明, 王晓玲, 朱涛, 等. 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. (Lin Yuming, Wang Xiaoling, Zhu Tao, et al. Survey on Quality Evaluation and Control of Online Reviews [J]. Journal of Software, 2014, 25(3): 506-527.)
[6] 孟美任, 丁晟春. 虚假商品评论信息发布者行为动机分析[J]. 情报科学, 2013, 31(10): 100-104. (Meng Meiren, Ding Shengchun. Motivation and Behavior of the Fraud Reviews' Publishers [J]. Information Science, 2013, 31(10): 100-104.)
[7] Liu B. Opinion Spam Detection: Detecting Fake Reviews and Reviewers [EB/OL]. [2013-11-12]. http://www.cs.uic.edu/~ liub/FBS/fake-reviews.html.
[8] Lim E, Nguyen V, Jindal N, et al. Detecting Product Review Spammers Using Rating Behaviors [C] . In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM'10). New York: ACM, 2010: 939-948.
[9] Wang G, Xie S, Liu B, et al. Review Graph Based Online Store Review Spammer Detection [C]. In: Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM'11). Washington, D C: IEEE Computer Society, 2011: 1242-1247.
[10] Algur S P, Patil A P, Hiremath P S, et al. Conceptual Level Similarity Measure Based Review Spam Detection [C]. In: Proceedings of the 2010 International Conference on Signal and Image Processing. IEEE, 2010: 416-423.
[11] Lai C L, Xu K Q, Lau R Y K, et al. Toward a Language Modeling Approach for Consumer Review Spam Detection [C]. In: Proceedings of the 2010 IEEE 7th International Conference on e-Business Engineering. IEEE, 2010: 1-8.
[12] Mihalcea R, Strapparava C. The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language [C]. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers. Stroudsburg: Association for Computational Linguistics, 2009: 309-312.
[13] 李志宇. 在线商品评论效用排序模型研究[J]. 现代图书情报技术, 2013 (4): 62-68. (Li Zhiyu.Study on the Reviews Effectiveness Sequencing Model of Online Products [J]. New Technology of Library and Information Service, 2013 (4): 62-68.)
[14] HowNet [EB/OL]. [2013-09-10]. http://www.keenage.com/html/ c_index.html.
[15] 数据堂. 台湾大学NTUSD - 简体中文情感极性词典[EB/OL]. [2013-10-11]. http://www.datatang.com/data/11837. (Data Tang.National Taiwan University-The Polarity of Simplified Chinese Emotional Dictionary [EB/OL]. [2013- 10-11]. http://www.datatang.com/data/11837.)
[16] Mukherjee A, Liu B, Glance N. Spotting Fake Reviewer Groups in Consumer Reviews [C]. In: Proceedings of the 21st International Conference on World Wide Web (WWW'12). New York: ACM, 2012: 191-200.
[17] 孙劲光, 马志芳, 孟祥福. 基于情感词属性和云模型的文本情感分类方法[J]. 计算机工程, 2013, 39(12): 211-215, 222. (Sun Jinguang, Ma Zhifang, Meng Xiangfu. Classification Method of Texts Sentiment Based on Sentiment Word Attributes and Cloud Model [J]. Computer Engineering, 2013, 39(12): 211-215, 222.)
[18] 开源中国社区. R语言[EB/OL]. [2013-12-14]. http://www. oschina.net/p/r-language/. (OSChina. R Language[EB/OL]. [2013-12-14]. http://www.oschina.net/ p/r-language/.)
[19] Rwordseg [EB/OL]. [2013-11-22]. http://jliblog.com/app/rwordseg/comment-page-1.
[20] Reviewers. Coffee_review [EB/OL]. [2014-04-03]. http:// item.taobao.com/item.htm?id=19728263832.
