A Framework for Identifying False Reviews in E-Commerce Platforms using Machine Learning
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A Framework for Identifying False Reviews in E-Commerce Platforms using Machine Learning
Dr. K. Satyam1, Cheerla Vanitha2
1Associate Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati,Andhra Pradesh, India.
2 Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, AndhraPradesh, India.
Abstract:Online reviews now have a much greater impact on consumer purchase decisions due to the quick development of ecommerce platforms. However, the existence of fraudulent or misleading evaluations has grown to be a significant problem, impacting the dependability and credibility of online marketplaces. By examining textual review data and related metadata, this study suggests a machine learning-based method for identifying fraudulent reviews on e-commerce sites.The suggested approach uses supervised machine learning methods for classification, text vectorization techniques for feature extraction, and data preprocessing. Users can input bulk review datasets using CSV files or examine individual reviews using the system's web-based application. To increase forecast reliability, the system also incorporates confidence score analysis and duplicate review detection. The model successfully divides reviews into authentic, fraudulent, and neutral categories while offering comprehensive insights into review patterns, according to experimental data. The created system can help e-commerce sites detect false reviews and enhance online feedback systems' reliability.
Keywords:Fake Review Detection, Machine Learning, Text Classification, E-Commerce Reviews, Natural Language Processing,TF-IDF Vectorization, Review Analysis, Duplicate Review Detection
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