The Performance of Machine and Deep Learning Algorithms in Detecting Fake Reviews

The advent of the Internet has enabled everyone with access to it to provide their views online. This freedom of expression has also resulted in an increasing amount of unstructured text data daily, which can be leveraged to build models that can help make better business decisions. Customer reviews have become an integral part of the decision-making process as there is a tremendous increase in online products and services. Reviews provided by users online have a major problem regarding reliability and authenticity. It is an arduous task to make business decisions based on unstructured reviews whose trustworthiness is not established. Hence, this paper focuses on classifying the reviews of certain restaurants available on the Internet using different machine/deep learning techniques and summarises the findings. The results show that deep learning methods are more efficient in identifying fake reviews. More specifically, combining BERT and a 4-layered Feed Forward network gave 96% accuracy in detecting fake reviews.