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Research Article

Fake Customer Review Detection System

Open Access Peer Reviewed Crossref DOI

Authors

  • Abhishek Kumar Roy1
  • Devsharan Singh2
  • Imran Raeeni3
  • Izmamul Ansari4

1234 B. Tech Students, Computer Science and Engineering, Institute of Technology and Management, Gorakhpur, U.P, India.

Article Information

Published Online
May-June 2023
Pages
150-160
Article Type
Research Article
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References

1. Estimating the incidence of fraud in online review communities: Ott M., Cardie, C., Hancock, J. 201210 is found in the book
Proceedings of the 21st International World Wide Web Conference. ACM (2012)
2. Anonymity, social image, and volunteer recruitment: a case study of the internet market for reviews, Wang, Z. B.E. Journal of
Economic Analysis, 10(1), 133 (2010)
3. Ott, M., Choi, Y., Cardie, and J.T. Hancock: Finding misleading opinion spam by whatever means necessary. In: Human Language
Technologies, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Portland, Oregon, USA, pp.
309319 (2011)
4. Time series-based fake opinion detection by Heydari, Tavakoli, and Salim. 58, 8392 Expert Syst. Appl (2016) Using rating
behaviours, Lim, E.-P., Nguyen, V.-A., Jindal, N., Liu, B., and Lauw, H.W., were able to identify spammy product reviews. 19th ACM
International Conference on Information Proceedings.
5. Xie, S., Wang, G., Lin, S., Yu, P.S.: Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM
International Conference on Knowledge Discovery and Data Mining, pp. 823831. ACM (2012)
6. Ye, J., Kumar, S., Akoglu, L.: Temporal opinion spam detection by multivariate indicative signals. In: ICWSM, pp. 743746 (2016)
7. Damien Rolon-Merette, Matt Ross, Thadde Rolon-Merette, Kinsey Church, “INTRODUCTION TO ANACONDA AND PYTHON:
INSTALLATION AND SETUP”, Volume:16/Issues:05/2020.
8. Stefan van der Walt, S. Chris Colbert, Gael Varoquaux, “THE NUMPY ARRAY: A STRUCTURE FOR EFFICIENT
NUMERICALCOMPUTION” 8 Feb-2011.
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