What is relevant in a text document a machine learning based approach
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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| Idioma: | English |
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Brac University
2021
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| Acesso em linha: | http://hdl.handle.net/10361/14976 |
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10361-149762022-01-26T10:10:26Z What is relevant in a text document a machine learning based approach Mahmud, Abdullah Al Noor, Jannat-E Reshad, Sadman Alam Fuad, Syed Nafis Chakrabarty, Amitabha Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University CNN SVM Gaussian Na¨ıve Bayes Text classification Machine learning 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 20-21). Text Documents often contain valuable data. But not all data is relevant. That is why extracting relevant data from text documents is an essential task. Extracting relevant data from text documents refers to the study of classifying text documents into such groups that describe the contents of documents. There are many methods to find out relevant data from a cluster of text or a text document. Classifying extensive textual data helps to organize the records better, make the search easier and relevant and simplify navigation. That makes this task still an open research issue. This paper uses three techniques of classifying text documents: convolution neural networks (CNN) with deep learning, Gaussian Na¨ıve Bayes and support vector machines (SVM). With these three algorithms, the text we want to classify goes through three layers of checks. So, it gives us more reliability. Abdullah Al Mahmud Jannat-E-Noor Sadman Alam Reshad . Syed Nafis Fuad B. Computer Science 2021-09-06T06:28:35Z 2021-09-06T06:28:35Z 2021 2021-06 Thesis ID 17301033 ID 17101021 ID 17101403 ID 17101250 http://hdl.handle.net/10361/14976 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. 22 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
CNN SVM Gaussian Na¨ıve Bayes Text classification Machine learning |
| spellingShingle |
CNN SVM Gaussian Na¨ıve Bayes Text classification Machine learning Mahmud, Abdullah Al Noor, Jannat-E Reshad, Sadman Alam Fuad, Syed Nafis What is relevant in a text document a machine learning based approach |
| 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 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Mahmud, Abdullah Al Noor, Jannat-E Reshad, Sadman Alam Fuad, Syed Nafis |
| format |
Thesis |
| author |
Mahmud, Abdullah Al Noor, Jannat-E Reshad, Sadman Alam Fuad, Syed Nafis |
| author_sort |
Mahmud, Abdullah Al |
| title |
What is relevant in a text document a machine learning based approach |
| title_short |
What is relevant in a text document a machine learning based approach |
| title_full |
What is relevant in a text document a machine learning based approach |
| title_fullStr |
What is relevant in a text document a machine learning based approach |
| title_full_unstemmed |
What is relevant in a text document a machine learning based approach |
| title_sort |
what is relevant in a text document a machine learning based approach |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14976 |
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