Detection of Deceptive Reviews Using Long Short-Term Memory (LSTM) and Deep Neural Network (DNN)

Abstract

Online reviews serve as social proof for potential customers, promoting confidence in businesses. The online marketplace is expanding rapidly on a global scale, with consumers increasingly relying on internet reviews. This influence is especially significant in the digital age, where customers often rely on the opinions of others to guide their purchasing decisions. As these reviews play a crucial role in shaping purchase decisions, some unethical companies are motivated to fabricate and distribute misleading evaluations. Deceptive reviews are fabricated evaluations produced with the intention of appearing real and misleading the consumers. Those deceptive reviews can be detected manually based on their patterns which are seen in their linguistic and psychological aspects. However, the deep learning techniques proposed outperform all conventional approaches and offer higher self-adaptability to extract the desired features implicitly. For the purpose of detecting false reviews, we have suggested a Deep Neural Network (DNN) based Deceptive Review Detection Model (DRDM) method.