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

Gupta, Priyanka, Gandhi, Shriya and Chakravarthi, Bharathi Raja (2022) Leveraging Transfer learning techniques- BERT, RoBERTa, ALBERT and DistilBERT for Fake Review Detection. In: FIRE 2021: Forum for Information Retrieval Evaluation. ACM International Conference Proceedings Series . Association for Computing Machinery, New York, pp. 75-82. ISBN 978-1-4503-9596-0

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Abstract

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.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software H Social Sciences > HF Commerce > Electronic Commerce
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Clara Chan
Date Deposited:	05 Jan 2022 18:24
Last Modified:	31 Jan 2022 14:10
URI:	https://norma.ncirl.ie/id/eprint/5260

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