FAKE REVIEW DETECTION USING DEEP LEARNING

With the exponential growth of online user-generated content, the issue of fake reviews has become a significant concern, impacting consumer decisions and trust in online platforms. Detecting fake reviews manually is challenging due to the sheer volume of reviews generated daily. This paper proposes a novel approach utilizing deep learning techniques for the automated detection of fake reviews. The study focuses on employing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract meaningful features from textual and contextual information within reviews. The proposed model integrates word embeddings and attention mechanisms to capture intricate patterns and dependencies within review texts. Furthermore, the research leverages a dataset of labeled reviews, distinguishing between genuine and fake reviews using various linguistic, behavioral, and sentiment-based features. The model is trained, validated, and fine-tuned using this dataset to enhance its ability to generalize across different review platforms and domains. Experimental results demonstrate the efficacy of the proposed deep learning model in accurately identifying fake reviews, achieving state-of-the-art performance metrics such as precision, recall, and F1-score.