  Online shopping stores have grown steadily over the past few years. Due to
the massive growth of these businesses, the detection of fake reviews has
attracted attention. Fake reviews are seriously trying to mislead customers and
thereby undermine the honesty and authenticity of online shopping environments.
So far, various fake review classifiers have been proposed that take into
account the actual content of the review. To improve the accuracies of existing
fake review classification or detection approaches, we propose to use BERT
(Bidirectional Encoder Representation from Transformers) model to extract word
embeddings from texts (i.e. reviews). Word embeddings are obtained in various
basic methods such as SVM (Support vector machine), Random Forests, Naive
Bayes, and others. The confusion matrix method was also taken into account to
evaluate and graphically represent the results. The results indicate that the
SVM classifiers outperform the others in terms of accuracy and f1-score with an
accuracy of 87.81%, which is 7.6% higher than the classifier used in the
previous study [5].

Computation and Language;Artificial Intelligence;Information Retrieval