Bang-lish sentiment classification using deep learning

This internship report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Podrobná bibliografie
Hlavní autoři:	Saleheen, Abrar, Siam, Saleh Ahmed, Akter, Sanjeda, Ghosh, Akash, Ahona, Adiba Anbar
Další autoři:	Shakil, Arif
Médium:	Diplomová práce
Jazyk:	English
Vydáno:	Brac University 2023
Témata:	Sentiment classification Spell corrector Hybrid model Convolutional Neural Network Gated recurrent unit Machine learning Cognitive learning theory (Deep learning)
On-line přístup:	http://hdl.handle.net/10361/19372

id	10361-19372
record_format	dspace
spelling	10361-193722023-08-09T21:02:05Z Bang-lish sentiment classification using deep learning Saleheen, Abrar Siam, Saleh Ahmed Akter, Sanjeda Ghosh, Akash Ahona, Adiba Anbar Shakil, Arif Rasel, Annajiat Alim Department of Computer Science and Engineering, Brac University Sentiment classification Spell corrector Hybrid model Convolutional Neural Network Gated recurrent unit Machine learning Cognitive learning theory (Deep learning) This internship report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-44). E-commerce websites and social media platforms have become integral parts of peo ple’s social lives. Through posts, comments, and reviews on social media and online shopping websites, people can share their ideas. Understanding people’s opinions and evaluating input requires the ability to classify sentiment. A variety of deep learning methods have been employed over time to categorize sentiment. Bang-lish, which consists of Bengali words printed in English letters, has gotten very little notice nonetheless. In addition, the majority of Bengali-speaking individuals utilize Bang-lish to post evaluations on e-commerce platforms. Understanding customers’ ideas are crucial for sellers who want to improve their goods. Bang-lish, however, is challenging to understand and evaluate since it lacks a set grammar. Convolutional neural networks (CNN) and gated recurrent units (GRU), two types of deep learning, are combined in this study’s proposed hybrid framework. Additionally, during the data preprocessing stage, we created a spell check algorithm for the most frequently used words and eliminated Bang-lish stop-words. In binary sentiment classification, our suggested model achieved 89% accuracy, 88% precision, 89% recall, and an 89% F1 score. Abrar Saleheen Saleh Ahmed Siam Sanjeda Akter Akash Ghosh Adiba Anbar Ahona B. Computer Science and Engineering 2023-08-09T08:29:33Z 2023-08-09T08:29:33Z 2023 2023-01 Thesis ID: 19101331 ID: 19101140 ID: 19101258 ID: 19101425 ID: 19101257 http://hdl.handle.net/10361/19372 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. 44 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Sentiment classification Spell corrector Hybrid model Convolutional Neural Network Gated recurrent unit Machine learning Cognitive learning theory (Deep learning)
spellingShingle	Sentiment classification Spell corrector Hybrid model Convolutional Neural Network Gated recurrent unit Machine learning Cognitive learning theory (Deep learning) Saleheen, Abrar Siam, Saleh Ahmed Akter, Sanjeda Ghosh, Akash Ahona, Adiba Anbar Bang-lish sentiment classification using deep learning
description	This internship report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Shakil, Arif
author_facet	Shakil, Arif Saleheen, Abrar Siam, Saleh Ahmed Akter, Sanjeda Ghosh, Akash Ahona, Adiba Anbar
format	Thesis
author	Saleheen, Abrar Siam, Saleh Ahmed Akter, Sanjeda Ghosh, Akash Ahona, Adiba Anbar
author_sort	Saleheen, Abrar
title	Bang-lish sentiment classification using deep learning
title_short	Bang-lish sentiment classification using deep learning
title_full	Bang-lish sentiment classification using deep learning
title_fullStr	Bang-lish sentiment classification using deep learning
title_full_unstemmed	Bang-lish sentiment classification using deep learning
title_sort	bang-lish sentiment classification using deep learning
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/19372
work_keys_str_mv	AT saleheenabrar banglishsentimentclassificationusingdeeplearning AT siamsalehahmed banglishsentimentclassificationusingdeeplearning AT aktersanjeda banglishsentimentclassificationusingdeeplearning AT ghoshakash banglishsentimentclassificationusingdeeplearning AT ahonaadibaanbar banglishsentimentclassificationusingdeeplearning
_version_	1814308561842339840

Bang-lish sentiment classification using deep learning

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