PV-DAE: A hybrid model for deceptive opinion spam based on neural network architectures

摘要

Opinion review is of great importance for both customers and organizations. Indeed, it helps customers in buying decisions and represents a valuable feedback for the companies, allowing them to improve their productions. However, numerous greedy companies resort to fake reviews in order to influence the customer and brighten the brand image, or to defame the one of their competitors. Various models are proposed in order to detect deceptive opinion reviews. Most of these models adopt traditional methods focusing on feature extraction and traditional classifiers. Unfortunately, these models do not capture the semantic aspect while ignoring the opinion's context. In order to tackle this issue, we propose a new approach based on Paragraph Vector Distributed Bag of Words (PV-DBOW) and the Denoising Autoencoder (DAE). The proposed customized model provides a strong representation which is based on a global representation of the opinions while preserving their semantics. Indeed, the embedding vectors capture the semantic meaning of all words in the context of each opinion. The generated review representations are fed into a fully connected neural network in order to detect deceptive opinion spam. The obtained results concerning the deception dataset show that our model is effective and outperforms the existing state-of-the-art methodologies. (C) 2020 Elsevier Ltd. All rights reserved.