Use of Supervised Machine Learning Classifiers for Online Fake Review Detection
Keywords:
Fake Reviews, Spam Detection Machine Learning, ML Classification, Naïve Bayes, SVMAbstract
Social media and e-commerce sites have prompted online communities to use reviews to provide input on goods, products, and services, as well as to support people to analyze customer input for buying choices, and corporations to improve manufacturing quality. Internet shoppers support or degrade the reputation of competitive brands. However, the dissemination of fake reviews fools people, making these reviews a worrying problem. This study proposes a guided learning online textual content fraudulent review detecting method. The work splits bogus data using machine learning classifiers and honest reviews. Experimental findings are compared to assessment measures. Planned system performance is compared to the baseline. The research comes to the conclusion that supervised machine learning techniques may be useful in identifying fraudulent reviews, but how well these techniques work is largely reliant on the characteristics that are chosen. In terms of accuracy, AUC, and other performance metrics, the SVM classifier with N-Gram feature extraction and CV feature selection performs better than other classifiers and feature selection techniques. This is shown by the examination of various feature extraction and selection techniques. According to the research, N-Gram feature extraction and CV feature selection may be helpful in spotting fraudulent reviews on e-commerce platforms. This would assist customers in making wise selections and increase the reliability of online reviews.
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