  This paper investigates the potential of semi-supervised Generative
Adversarial Networks (GANs) to fine-tune pretrained language models in order to
classify Bengali fake reviews from real reviews with a few annotated data. With
the rise of social media and e-commerce, the ability to detect fake or
deceptive reviews is becoming increasingly important in order to protect
consumers from being misled by false information. Any machine learning model
will have trouble identifying a fake review, especially for a low resource
language like Bengali. We have demonstrated that the proposed semi-supervised
GAN-LM architecture (generative adversarial network on top of a pretrained
language model) is a viable solution in classifying Bengali fake reviews as the
experimental results suggest that even with only 1024 annotated samples,
BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and
a f1-score of 84.89% outperforming other pretrained language models -
BanglaBERT generator, Bangla BERT Base and Bangla-Electra by almost 3%, 4% and
10% respectively in terms of accuracy. The experiments were conducted on a
manually labeled food review dataset consisting of total 6014 real and fake
reviews collected from various social media groups. Researchers that are
experiencing difficulty recognizing not just fake reviews but other
classification issues owing to a lack of labeled data may find a solution in
our proposed methodology.
