Unveiling Deception: A Fusion of Deep Learning and Sentiment Analysis for Identifying Counterfeit Reviews - Kumar, Jitender; Singh, Baldev (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In the contemporary landscape of consumer decision-making, the influence of online reviews is paramount. However, the authenticity of these reviews has become a pressing concern. This study proposes a comprehensive strategy for identifying counterfeit reviews on online platforms by integrating advanced deep learning techniques with sentiment analysis. The primary objective is to develop a model capable of distinguishing between deceptive and genuine reviews. The methodology includes data acquisition, preprocessing, and the application of a neural network model featuring key elements such as an Embedding layer for word representations, a Convolutional layer for feature extraction, a Long Short-Term Memory (LSTM) layer for capturing sequential dependencies, and a Dense output layer for binary classification. To evaluate the model’s effectiveness, a dataset comprising categorized reviews is utilized. The dataset is split into training and testing subsets, and the model undergoes training across multiple epochs, with continuous monitoring of metrics like loss and accuracy. Visual representations illustrate the model’s training progress. Additionally, the study incorporates sentiment analysis using the VADER tool to assess the emotional tone of reviews, aiding in the differentiation between authentic and fabricated sentiments. The research findings highlight the efficacy of the combined deep learning and sentiment analysis approach in detecting counterfeit reviews. The model exhibits competitive performance in review classification, potentially enhancing trustworthiness on online platforms. The sentiment analysis component enriches our understanding of user sentiments, providing a deeper insight into review content. By offering a robust and interpretable model alongside a comprehensive methodology, this research significantly contributes to the field of counterfeit review detection in the digital era.