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Research Article
Fake Customer Review Detection System
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.
Published Online: May-June 2023
Pages: 150-160
Cite this article
↗ 10.59256/ijire.2023040366
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.
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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