DETECTION OF FAKE ONLINE REVIEWS USING SEMI SUPERVISED AND SUPERVISED MACHINE LEARNING MODELS

Authors

  • Kondragunta Rama Krishnaiah, Harish H

Keywords:

Fake Reviews Detection, Machine Learning, Supervised Learning, Semi Supervised Learning, Sentiment Analysis.

Abstract

In the era of digital commerce, online reviews play a pivotal role in influencing consumerpurchasing decisions. However, the increasing prevalence of fake reviews has raised concernsregarding the authenticity of product feedback. This study presents a hybrid approach to
detecting fake online reviews by utilizing both semi-supervised and supervised machinelearning models

References

R. Oak and Z. Shafiq, “The Fault in the Stars: Understanding Underground Incentivized Review Services,” arXiv (Cornell University), Jan. 2021, doi: 10.48550/arXiv.2102.04217.

T. a p Sinnasamy and N. N. A. Sjaif, “A Survey on Sentiment Analysis Approaches in eCommerce,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 10, Jan. 2021, doi: 10.14569/ijacsa.2021.0121074.

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Published

2024-03-15

How to Cite

Kondragunta Rama Krishnaiah, Harish H. (2024). DETECTION OF FAKE ONLINE REVIEWS USING SEMI SUPERVISED AND SUPERVISED MACHINE LEARNING MODELS. Journal of Computational Analysis and Applications (JoCAAA), 33(4), 815–823. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2667

Issue

Vol. 33 No. 4 (2024): Selected Papers on Computational Techniques Across Multidisciplinary Fields: Innovations and Applications

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.