  Online reviews have a significant influence on customers' purchasing
decisions for any products or services. However, fake reviews can mislead both
consumers and companies. Several models have been developed to detect fake
reviews using machine learning approaches. Many of these models have some
limitations resulting in low accuracy in distinguishing between fake and
genuine reviews. These models focused only on linguistic features to detect
fake reviews and failed to capture the semantic meaning of the reviews. To deal
with this, this paper proposes a new ensemble model that employs transformer
architecture to discover the hidden patterns in a sequence of fake reviews and
detect them precisely. The proposed approach combines three transformer models
to improve the robustness of fake and genuine behaviour profiling and modelling
to detect fake reviews. The experimental results using semi-real benchmark
datasets showed the superiority of the proposed model over state-of-the-art
models.
