An integrated approach: fake review detection using convBERT-BiLSTM classification

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Bibliografiske detaljer
Main Authors:	Mahmud, Md. Anas, Hasan, Alina, Mahbub, Tajrian, Rafi, Navid Hasan, Faiaz, Rushayed Ali
Andre forfattere:	Rahman, Md. Khalilur
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2024
Fag:	Natural language processing Fake review detection Neural networks BERT ConvBERT BiLSTM Natural language processing (Computer science) Deep learning (Machine learning)
Online adgang:	http://hdl.handle.net/10361/22855

id	10361-22855
record_format	dspace
spelling	10361-228552024-05-16T21:03:20Z An integrated approach: fake review detection using convBERT-BiLSTM classification Mahmud, Md. Anas Hasan, Alina Mahbub, Tajrian Rafi, Navid Hasan Faiaz, Rushayed Ali Rahman, Md. Khalilur Department of Computer Science and Engineering, Brac University Natural language processing Fake review detection Neural networks BERT ConvBERT BiLSTM Natural language processing (Computer science) Deep learning (Machine learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-32). 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. Md. Anas Mahmud Alina Hasan Tajrian Mahbub Navid Hasan Rafi Rushayed Ali Faiaz B.Sc in Computer Science 2024-05-16T10:04:56Z 2024-05-16T10:04:56Z ©2024 2024-01 Thesis ID: 20101149 ID: 20101301 ID: 20101325 ID: 20101585 ID: 21301717 http://hdl.handle.net/10361/22855 en Brac 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. 43 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Natural language processing Fake review detection Neural networks BERT ConvBERT BiLSTM Natural language processing (Computer science) Deep learning (Machine learning)
spellingShingle	Natural language processing Fake review detection Neural networks BERT ConvBERT BiLSTM Natural language processing (Computer science) Deep learning (Machine learning) Mahmud, Md. Anas Hasan, Alina Mahbub, Tajrian Rafi, Navid Hasan Faiaz, Rushayed Ali An integrated approach: fake review detection using convBERT-BiLSTM classification
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Rahman, Md. Khalilur
author_facet	Rahman, Md. Khalilur Mahmud, Md. Anas Hasan, Alina Mahbub, Tajrian Rafi, Navid Hasan Faiaz, Rushayed Ali
format	Thesis
author	Mahmud, Md. Anas Hasan, Alina Mahbub, Tajrian Rafi, Navid Hasan Faiaz, Rushayed Ali
author_sort	Mahmud, Md. Anas
title	An integrated approach: fake review detection using convBERT-BiLSTM classification
title_short	An integrated approach: fake review detection using convBERT-BiLSTM classification
title_full	An integrated approach: fake review detection using convBERT-BiLSTM classification
title_fullStr	An integrated approach: fake review detection using convBERT-BiLSTM classification
title_full_unstemmed	An integrated approach: fake review detection using convBERT-BiLSTM classification
title_sort	integrated approach: fake review detection using convbert-bilstm classification
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22855
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_version_	1814308994636840960

An integrated approach: fake review detection using convBERT-BiLSTM classification

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