Hybrid AI Model for Fake Product Review Identification

Fake reviews have become a major concern in online platforms, influencing consumer decisions and undermining trust. This study explores the effectiveness of machine learning and OpenAI’s GPT-4 in detecting fraudulent reviews. Traditional machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Naïve Bayes, are trained on text data using TF-IDF feature extraction. Additionally, Open AI’s GPT-4 is leveraged for text classification, and a hybrid model combining both approaches is proposed using a Voting Classifier. Performance metrics such as accuracy, precision, and F1-score are evaluated for each model, with results visualized through comparative analysis. The proposed method demonstrates integrating of OpenAI’s language models with traditional machine learning techniques which improves overall detection accuracy.