Enhancing Product Review Authenticity Detection with Ensemble Learning and BERT Model

This study explores the integration of TF-IDF, count vectorization, and the BERT model within the context of fake review detection. TF-IDF and count vectorization, renowned for their ability to capture linguistic nuances, are employed as feature extraction techniques to encode the distinguishing characteristics of genuine and deceptive reviews. Through rigorous experimentation, we evaluate the efficacy of these methods in enhancing the discriminatory power of fake review detection systems. Our findings demonstrate that the fusion of TF-IDF, count vectorization, and the BERT model yields a formidable framework capable of discerning subtle linguistic cues indicative of fraudulent content. Leveraging the complementary strengths of these techniques, our approach offers both interpretability and scalability, thereby advancing the state-of-the-art in fake review detection. This research contributes to the ongoing efforts to fortify the integrity of online information ecosystems and uphold trust and credibility in digital discourse.