Fake Reviews Identification Using Deep Learning Techniques

Fake online reviews pose a significant challenge to e-commerce platforms by misleading consumersand damaging business credibility. These reviews distort market perceptions, influence purchasingdecisions, and manipulate product ratings. Deep learning techniques have emerged as powerful tools todetect fraudulent reviews with higher accuracy and efficiency.This study explores the application of advanced deep learning models—Long Short-Term Memory(LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and BidirectionalEncoder Representations from Transformers (BERT)—for fake review detection. Using the "FakeReviews Dataset" from Kaggle, consisting of 40,433 reviews, we evaluate the effectiveness of thesemodels with enhanced text preprocessing techniques, including tokenization, stop word removal, andlemmatization.The LSTM model achieves superior accuracy, effectively capturing sequential dependencies in text.achieving higher accuracy and effectively capturing sequential dependencies in textual data. CNN isutilized for pattern recognition, RNN processes sequential text-based dependencies, and BERT is exploredfor its contextual understanding of language. The evaluation metrics—precision, recall, and F1-score—further validate the performance of each deep learning model.The findings of this research emphasize the potential of deep learning in strengthening the reliability ofonline reviews, fostering transparency in e-commerce, and protecting consumer trust. This studycontributes to the advancement of fake review detection methodologies, highlighting the significance ofdeep learning in combating digital misinformation.