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Detection of Online Fake Review in E-Commerce
Authors
1234Student, Department of Computer Science and Engineering, Vivekanandha College of Engineering for women, Tiruchengode, Naraka District, Tamil Nadu, 637205, India. 5Assistant professor, Department of Computer Science and Engineering, Vivekanandha College of Engineering for women, Tiruchengode, Namakkal District, Tamil Nadu,637205,India.
Article Information
- DOI
- No DOI
- Published In
- Volume 3, Issue 3 (2022)
- Published Online
- May-June 2022
- Pages
- 278-282
- Article Type
- Research Article
- Check for updates
- No DOI available for cross-reference
References
[1] Barbosa, Luciano & Feng, Junlan. (2010). Robust Sentiment Detection on Twitter from Biased and oisy Data. Coling 2010 - 23rd
International Conference on Computational Linguistics, Proceedings of the Conference. 2. 36-44.
[2] Enhanced Sentiment Learning Using Twitter Hashtags and Smileys Dmitry Davidov, Oren Tsur, ICNC / 2, Institute of Computer Science The Hebrew University 2010.
[3] Go, Alec & Bhayani, Richa & Huang, Lei. (2009). Twitter sentiment classification using distant supervision. Processing. 150.
[4] “Fake review detection using opinion mining” by Dhairya Patel, Aishwerya Kapoor and SameetSonawane, International Research
journal of Engineering and technology (IRJET) , volume 5, issue 12,Dec 2018.
[5] Ravi, k. Ravi., 2015. A survey on opinion mining and sentiment analysis:Tasks, approaches and applications. Knowledge based systems,89.14-46.
[6] Khan, K. et al., “Mining opinion components from unstructured reviews: A
review”. Journal of King Saud University – Computer and Information Sciences (2014), http://dx.doi.org/10.1016/j.jksuci.2014.03.009.
[7] “Fake review detection from product review using modified method of iterative computation framework”, by
EkaDyarWahyuni&ArifDjunaidy, MATEC web conferences 58.03003(2016) BISSTECH 2015.
[8] Saumya, S., Singh, J.P. Detection of spam reviews: a sentiment analysis approach. CSIT 6, 137–148 (2018).
https://doi.org/10.1007/s40012-018- 0193-0.
[9] Xie S, Wang G, Lin S, Yu PS (2012) Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD
international Conference on Knowledge discovery and data mining. ACM, pp 823–831.
[10] Mukherjee A, Venkataraman V, Liu B, Glance N (2013a) Fake review detection: classification and analysis of real and pseudo reviews.Technical Report UIC-CS-2013–03, University of Illinois at Chicago.
International Conference on Computational Linguistics, Proceedings of the Conference. 2. 36-44.
[2] Enhanced Sentiment Learning Using Twitter Hashtags and Smileys Dmitry Davidov, Oren Tsur, ICNC / 2, Institute of Computer Science The Hebrew University 2010.
[3] Go, Alec & Bhayani, Richa & Huang, Lei. (2009). Twitter sentiment classification using distant supervision. Processing. 150.
[4] “Fake review detection using opinion mining” by Dhairya Patel, Aishwerya Kapoor and SameetSonawane, International Research
journal of Engineering and technology (IRJET) , volume 5, issue 12,Dec 2018.
[5] Ravi, k. Ravi., 2015. A survey on opinion mining and sentiment analysis:Tasks, approaches and applications. Knowledge based systems,89.14-46.
[6] Khan, K. et al., “Mining opinion components from unstructured reviews: A
review”. Journal of King Saud University – Computer and Information Sciences (2014), http://dx.doi.org/10.1016/j.jksuci.2014.03.009.
[7] “Fake review detection from product review using modified method of iterative computation framework”, by
EkaDyarWahyuni&ArifDjunaidy, MATEC web conferences 58.03003(2016) BISSTECH 2015.
[8] Saumya, S., Singh, J.P. Detection of spam reviews: a sentiment analysis approach. CSIT 6, 137–148 (2018).
https://doi.org/10.1007/s40012-018- 0193-0.
[9] Xie S, Wang G, Lin S, Yu PS (2012) Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD
international Conference on Knowledge discovery and data mining. ACM, pp 823–831.
[10] Mukherjee A, Venkataraman V, Liu B, Glance N (2013a) Fake review detection: classification and analysis of real and pseudo reviews.Technical Report UIC-CS-2013–03, University of Illinois at Chicago.
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