A framework of review data mining based on a graph model.
We implement six algorithms:
- Mutually Reinforcing Analysis (MRA) (rgmining-ria) 1,
- Repeated Improvement considering Heterogeneity (RIH) (rgmining-ria) 2,
- Detecting Product Review Spammers Using Rating Behaviors (rgmining-ria) 3,
- Review Spammer Detection (rgmining-rsd) 4,
- Fraud Eagle (rgmining-fraud-eagle) 5,
- FRAUDAR (rgmining-fraudar) 6.
Footnotes
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Kazuki Tawaramoto, Junpei Kawamoto, Yasuhito Asano, and Masatoshi Yoshikawa, "A Bipartite Graph Model and Mutually Reinforcing Analysis for Review Sites," Proc. of the 22nd International Conference on Database and Expert Systems Applications (DEXA 2011), pp.341-348, Toulouse, France, August 31, 2011. ↩
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Yasuhito Asano, Junpei Kawamoto, "Detecting Anomalous Reviewers and Estimating Summaries from Early Reviews Considering Heterogeneity," IEICE Transactions, 101-D(4), pp.1003-1011, April 2018. ↩
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Ee-Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu, Hady Wirawan Lauw, "Detecting Product Review Spammers Using Rating Behaviors," Proc. of the 19th ACM International Conference on Information and Knowledge Management, pp.939-948, 2010. ↩
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Guan Wang, Sihong Xie, Bing Liu, Philip S. Yu, "Review Graph Based Online Store Review Spammer Detection," Proc. of the 11th IEEE International Conference on Data Mining (ICDM 2011), pp.1242-1247, 2011. ↩
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Leman Akoglu, Rishi Chandy, and Christos Faloutsos, "Opinion Fraud Detection in Online Reviews by Network Effects," Proc. of the 7th International AAAI Conference on WeblogsS and Social Media (ICWSM 2013), Boston, MA, July, 2013. ↩
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Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos, "FRAUDAR: Bounding Graph Fraud in the Face of Camouflage," Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), pp.895-904, 2016. ↩
