BERT Model for Roman Urdu Fake Review Identification

Abstract

The variety of e-commerce and online business environments makes spotting fraud or fake reviews challenging. The goal of this research is to create methods that could ensure the authenticity of reviews. Numerous endeavors have already been made by the research community to address this problem. Unfortunately, these efforts were confined to a specific number of languages, such as English, Arabic, and a few others. Roman Urdu is being used across the subcontinent. For this task, this language hasn't been properly investigated. On the other hand, Transformer-based models have demonstrated tremendous success over the past few years for a variety of Natural Language Processing applications. Transformer-based models, however, have not been investigated for the goal of examining fake Roman Urdu reviews. This study compares multiple transfer-based models, including BERT_base and BERT_large with five deep learning models Simple RNN, LSTM, Bidirectional LSTM, GRU, and Bidirectional GRU aims to close this research gap. The evaluation was conducted using frequently applied metrics, i.e., ROC-Accuracy, F_1, Precision, and Recall. The BERT_large with Bi-LSTM model produced the best results (ROC-ACC=0.94 and F_1=0.94). Results from a previous study using the same corpus were improved by 6% in this investigation.