Rahman, Md. KhalilurMahmud, Md. AnasHasan, AlinaMahbub, TajrianRafi, Navid HasanFaiaz, Rushayed Ali2024-05-162024-05-16©20242024-01ID: 20101149ID: 20101301ID: 20101325ID: 20101585ID: 21301717http://hdl.handle.net/10361/22855Cataloged from PDF version of thesis.Includes bibliographical references (pages 29-32).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.In the era of E-commerce, online reviews significantly shape consumer buying decisions and store evaluations. However, the prevalence of unethical practices such as review manipulation poses a considerable challenge. Businesses often hire spam reviewers or deploy bots to boost their reputation or even damage that of their competitors. Despite existing efforts in the field of fake review detection, there remains a need for further studies. In contribution, we propose the development of a scoring rubric designed to guide annotators in the identification of fake reviews and a hybrid model ConvBERT-BiLSTM for detection. We leverage the efficiency of ConvBERT, a compact variant of the BERT model, and the superior capabilities of BiLSTM over LSTM. The model is trained on a dataset gathered from Amazon. The dataset comprises 7,727 labeled reviews using the rubric. Through careful assessment, the proposed model garnered an accuracy of 97% surpassing previously established BERT variants.43 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Natural language processingFake review detectionNeural networksBERTConvBERTBiLSTMNatural language processing (Computer science)Deep learning (Machine learning)Thesis