DHMFRD – TER: a deep hybrid model for fake review detection incorporating review texts, emotions, and ratings

Recently, there has been an increasing reward to manipulate product/ service reviews, mostly profit-driven, since positive reviews infer high business returns and vice versa. To combat this issue, experts in industry and researchers recently attempted integrating multi-aspect (reviewer- and review-centric) data features. However, the emotions hidden in the review, the semantic meaning of the review, and data heterogeneity still deserve more study as they are essential indicators of fake content. This study proposed a Deep Hybrid Model for Fake Review Detection incorporating review Texts, Emotions, and Ratings (DHMFRD – TER). Initially, it computes contextualized review text vectors and extraction of emotion indicators representations. Then, the model learns the representation to extract higher-level review features. Finally, contextualized word vectors, ratings, and emotions are concatenated; such a multidimensional feature representation is used to classify reviews. Extensive experiments on three publicly available datasets demonstrate that DHMFRD–TER significantly outperforms state-of-the-art baseline approaches, achieving an accuracy of 0.988, 0.987, and 0.994 in Amazon, Yelp CHI, and OSF datasets, respectively.