Fake Reviews Identification Using Deep Learning Techniques

Fake online reviews pose a significant challenge to e-commerce platforms by misleading consumers
and damaging business credibility. These reviews distort market perceptions, influence purchasing
decisions, and manipulate product ratings. Deep learning techniques have emerged as powerful tools to
detect 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 Bidirectional
Encoder Representations from Transformers (BERT)—for fake review detection. Using the "Fake
Reviews Dataset" from Kaggle, consisting of 40,433 reviews, we evaluate the effectiveness of these
models with enhanced text preprocessing techniques, including tokenization, stop word removal, and
lemmatization.
The LSTM model achieves superior accuracy, effectively capturing sequential dependencies in text.
achieving higher accuracy and effectively capturing sequential dependencies in textual data. CNN is
utilized for pattern recognition, RNN processes sequential text-based dependencies, and BERT is explored
for 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 of
online reviews, fostering transparency in e-commerce, and protecting consumer trust. This study
contributes to the advancement of fake review detection methodologies, highlighting the significance of
deep learning in combating digital misinformation.