Bangla text classification using machine learning and deep learning techniques
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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2021
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10361-156762022-01-26T10:15:57Z Bangla text classification using machine learning and deep learning techniques Salehin, Kamrus Ahmed, Fahim Nabi, Md. Ashifun Alam, M. Kaosar Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Bangla news articles Text categorization Classifiers Neural networks Comparison Machine learning Data mining This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 31-33). At present, we have seen everything is getting digitized where technology almost takes full control over our life. As a result, a massive number of textual documents are generated on online platforms and news articles are no exception. People prefer to get connected with online news portals as they are updated every single hour. Newspaper articles have so many categories such as politics, sports, business, entertainment, etc. Recently, we have noticed the rapid growth and increase of Bangla online news portals on the internet. It will be helpful for the online readers to get recommended the preferable news category which assists them in locating desired articles. Manually categorizing news articles takes a huge time and e ort. So, text categorization is necessary for the modern day, as enormous amounts of uncategorized data are an issue here. Although the study has improved in categorizing news articles greatly for languages such as English, Arabic, Chinese, Urdu, and Hindi. Among others, the Bangla language has shown little development. However, some approaches applied to categorize Bangla news articles, using some machine learning algorithms where resources were minimum. We have applied ve machine learning classi ers and two neural networks to categorize Bangla news articles. To show the comparison between applied algorithms, which one is performing better, we have used four metrics that measure performance. Kamrus Salehin Fahim Ahmed Md. Ashifun Nabi M. Kaosar Alam B. Computer Science 2021-12-01T04:17:04Z 2021-12-01T04:17:04Z 2021 2021-09 Thesis ID 17101164 ID 21341060 ID 17301152 ID 17301117 http://hdl.handle.net/10361/15676 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. 33 pages application/pdf Brac University |
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Brac University |
collection |
Institutional Repository |
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English |
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Bangla news articles Text categorization Classifiers Neural networks Comparison Machine learning Data mining |
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Bangla news articles Text categorization Classifiers Neural networks Comparison Machine learning Data mining Salehin, Kamrus Ahmed, Fahim Nabi, Md. Ashifun Alam, M. Kaosar Bangla text classification using machine learning and deep learning techniques |
description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
author2 |
Ashraf, Faisal Bin |
author_facet |
Ashraf, Faisal Bin Salehin, Kamrus Ahmed, Fahim Nabi, Md. Ashifun Alam, M. Kaosar |
format |
Thesis |
author |
Salehin, Kamrus Ahmed, Fahim Nabi, Md. Ashifun Alam, M. Kaosar |
author_sort |
Salehin, Kamrus |
title |
Bangla text classification using machine learning and deep learning techniques |
title_short |
Bangla text classification using machine learning and deep learning techniques |
title_full |
Bangla text classification using machine learning and deep learning techniques |
title_fullStr |
Bangla text classification using machine learning and deep learning techniques |
title_full_unstemmed |
Bangla text classification using machine learning and deep learning techniques |
title_sort |
bangla text classification using machine learning and deep learning techniques |
publisher |
Brac University |
publishDate |
2021 |
url |
http://hdl.handle.net/10361/15676 |
work_keys_str_mv |
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