[1]ZHAO Jun,WANG Hong.Detection of fake reviews based on emotional orientation and logistic regression[J].CAAI Transactions on Intelligent Systems,2016,11(3):336-342.[doi:10.11992/tis.201603027]
Copy

Detection of fake reviews based on emotional orientation and logistic regression

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 3 Page number: 336-342 Column: 学术论文—智能系统 Public date: 2016-06-25
Title:
Detection of fake reviews based on emotional orientation and logistic regression
Author(s):
ZHAO Jun1; 2;  WANG Hong1; 2
1. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China;
2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Ji’nan 250014, China
Keywords:
Electronic commerce; fake review; shopping behavior; emotional polarity; logic regression
CLC:
TP39
DOI:
10.11992/tis.201603027
Abstract:
Online shopping reviews provide valuable customer information for comparing the quality of products and several other aspects of future purchases. However, spammers are joining this community to mislead and confuse consumers by writing fake or unfair reviews. To detect the presence of spammers, reviewer styles have been scrutinized for text similarity and rating patterns. These studies have succeeded in identifying certain types of spammers. However, there are other spammers who can manipulate their behaviors such that they are indistinguishable from normal reviewers, and thus, they cannot be detected by available techniques. In this paper, we analyze the orientation of comments, extract different features, and use a logic regression model to detect false comments. First, we utilize natural language processing technology to analyze the orientation of comments and compute the departures of those comments from those of the general public. The greater is the deviation, the greater is the probability of the comment being generated by a spammer. Then, we select several other important features and combine them with the logic regression model to identify fake comments. The experimental results verify the greater accuracy of the proposed method.
References:
[1] KOLCZ A, ALSPECTOR J. SVM-based filtering of E-mail spam with content specific misclassification costs[C]//Proceedings of ICDM-2001 Workshop on Text Mining. Dallas, USA, 2001: 324-332.
[2] BECCHETTI L, CASTILLO C, DONATO D, et al. Link-based characterization and detection of web spam[C]//Adversarial Information Retrieval on the Web. Washington, USA, 2006: 1012-1021.
[3] JINDAL N, LIU Bing. Review spam detection[C]//Proceedings of the 16th International Conference on World Wide Web. Alberta, Canada, 2007: 1189-1190.
[4] JINDAL N, LIU Bing, et al. Opinion spam and analysis[C]//Proceedings of the 2008 International Conference on Web Search and Data Mining. California, USA, 2008: 219-230.
[5] WU Fang, HUBERMAN B A. Opinion information under costly express[J]. ACM transactions on intelligence systems and technology, 2010, 1(1): 5.
[6] 谭文堂, 朱洪, 葛斌, 等. 垃圾评论自动过滤方法[J]. 国防科技大学学报, 2012, 34(5): 153-157, 168. TAN Wentang, ZHU Hong, GE Bin, et al. Method of review spam detection[J]. Journal of national university of defense technology, 2012, 34(5): 153-157, 168.
[7] OTT M, CHOI Y, CARIDIE C, et al. Finding deceptive opinion spam by any stretch of the imagination[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: human language technologies. Portland, USA, 2011, 1: 309-319.
[8] 任亚峰, 尹兰, 姬东鸿. 基于语言结构和情感极性的虚假评论识别[J]. 计算机科学与探索, 2014, 8(3): 313-320. REN Yafeng, YIN Lan, JI Donghong. Deceptive reviews detection based on language structure and sentiment polarity[J]. Journal of frontiers of computer science and technology, 2014, 8(3): 313-320.
[9] WANG Guan, XIE Sihong, LIU Bing, et al. Identify online store review spammers via social review graph[J]. ACM Transactions on intelligent systems and technology, 2012, 3(4): 61.
[10] GAO Jian, DONG Yuwei, SHANG Mingsheng, et al. Group-based ranking method for online rating systems with spamming attacks[J]. EPL (europhysics letters), 2015, 110(2): 28003.
[11] 唐波, 陈光, 王星雅, 等. 微博新词发现及情感倾向性判断分析[J]. 山东大学学报:理学版, 2015, 50(1): 20-25. TANG Bo, CHEN Guang, WANG Xingya, et al. Analysis on new word detection and sentiment orientation in Micro-blog[J]. Journal of Shandong university: nature science, 2015, 50(1): 20-25.
[12] 何凤英. 基于语义理解的中文博文倾向性分析[J]. 计算机应用, 2011, 31(8): 2130-2133, 2137. HE Fengying. Orientation analysis for Chinese blog text based on semantic comprehension[J]. Journal of computer application, 2011, 31(8): 2130-2133, 2137.
[13] 邸鹏, 李爱萍, 段利国. 基于转折句式的文本情感倾向性分析[J]. 计算机工程与设计, 2014, 35(12): 4289-4295. DI Peng, LI Aiping, DUAN Liguo. Text sentiment polarity analysis based on transition sentence[J]. Computer engineering and design, 2014, 35(12): 4289-4295.
[14] FENG Song, BANERJEE R, CHOI Y. Syntactic stylometry for deception detection[C]//Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2. Jeju, Korea, 2012: 171-175.
[15] LI Jiwei, CARDIE C, LI Sujian. TopicSpam: a topic-model-based approach for spam detection[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Sofi, Bulgaria, 2013: 217-221.
[16] JINDAL N, LIU Bing, LIM E P. Finding unusual review patterns using unexpected rules[C]//Proceedings of the 19th ACM International Conference on Information and Knowledge Management. Ontario, Canada, 2010: 1549-1552.
[17] JO Y, OH A H. Aspect and sentiment unification model for online review analysis[C]//Proceedings of the 4th ACM International Conference on Web Search and Data Mining. New York, USA, 2011: 815-824.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems