Detection of Fake Online Reviews using Semi-Supervised and Supervised: Machine Learning Algorithms

Author(s)	Mr. BHAVANI SHANKAR PAGALLA, Dr. BABU REDDY M, Mr. Uday Chandra B, Mr. Bhargava Ram K, Mr. Rohith K, Mr. Lakshmi Chand A
Country	India
Abstract	Fake online reviews have become a significant challenge in e-commerce and online platforms, affecting consumer trust and business reputations. This study presents an approach to detecting fake reviews using both semi-supervised and supervised learning techniques. The proposed methodology leverages machine learning models trained on labeled and unlabeled datasets to improve classification accuracy. Experimental results demonstrate the effectiveness of various algorithms in distinguishing between genuine and fake reviews. The findings contribute to the development of robust fraud detection systems for online platforms. The study employs a combination of support vector machines (SVM), Navie's Bayes, and Expectation Maximization (EM) algorithms to detect false reviews. Performance is evaluated using metrics such as accuracy, precision, recall, and F1 score, showing that semi-supervised learning improves classification effectiveness by leveraging unlabeled data.
Keywords	semi-supervised learning, supervised learning, Navie Bayes Classifier, Support Vector Machine Classifier, Expectation-maximization algorithm.
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-26
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.42769
Short DOI	https://doi.org/g9gvf4

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International Journal For Multidisciplinary Research