Fake Review Detector – A Deep Learning Approach with Topic Modeling

Fake Review Detector - A Deep Learning Approach with Topic Modeling

B.Nikitha, B.Poojitha, K.Nithyasree, U.Nikhil, K.Pravallika, Prof.Maddi Sri.V.S.Suneeta

Abstract: The discovery of fake reviews is one of the major concerns for the E-commerce business-the Consumers on reviews before finalizing their choice for the product. This paper presents a new approach to detect fake reviews using deep learning techniques, specifically DenseNet, combined with topic modeling methods such as Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). DenseNet, a convolutional neural network architecture, is used to extract deep features from text data and capture complex patterns that indicate authenticity or fraud. At the same time, topic modeling techniques are used to reveal the underlying themes of the reviews, providing an additional perspective on the structure of the content. Integrating these methods allows for a robust and comprehensive detection system that outperforms traditional approaches. Experimental results demonstrate the effectiveness of our proposed model and its ability to accurately identify fake reviews from various datasets. This study highlights the potential of combining deep learning and topic modeling to improve the trustworthiness and reliability of online assessment platforms.