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Leveraging Transfer learning techniques- BERT, RoBERTa, ALBERT and DistilBERT for Fake Review Detection

In this era of the internet, the online review system has grown tremendously, where customers share their first-hand experiences about the products or services. These reviews influence the purchasing decision of future customers and have a positive or negative financial impact on businesses. Spam reviews are written with an agenda to promote or demote a business and mislead the customers. Hence to maintain the integrity of the online review system, it is crucial to detect fake reviews. To overcome the limitations of traditional machine learning and neural network − based models, we have leveraged transfer learning and used transformer-based pre-trained models BERT, RoBERTa, ALBERT, and DistilBERT to build fake review classifier. Performance of all the models is evaluated, considering accuracy and weighted F1-source as the primary metric for evaluation. The classifier produced using RoBERTa has outperformed the baseline model in detecting fake reviews