| Summary: | Detecting fake reviews is important for maintaining the authenticity and reliability of online platforms. In this project, we address the challenges of fake review detection using machine learning techniques, focusing on the application of DistilBERT model and adversarial sample generation. Our approach involves data preprocessing, which includes cleaning and augmentation, to ensure the quality and diversity of the dataset. This project used state-of-the-art technologies and modern tools to train and fine-tune the model and evaluate the performance in terms of precision, recall, F1-score, and accuracy.
This project highlights the significance of model training and evaluation methodologies to accurately detect between real and fake reviews. By combining adversarial samples into the training dataset, we enhance the model's resilience against manipulative inputs, ensuring its effectiveness in real-world scenarios. The outcomes of this project contribute to advancing fake review detection technologies, offering insights into leveraging machine learning for maintaining trust and credibility in online review systems.
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