Text classification using machine learning algorithms

Cataloged from PDF version of thesis.

Bibliografische gegevens
Hoofdauteurs:	Hasnat, Fahim, Hasan, Md. Mazidul, Khan, Nayeem Hasan, Ali, Asif
Andere auteurs:	Chakrabarty, Amitabha
Formaat:	Thesis
Taal:	English
Gepubliceerd in:	BRAC University 2018
Onderwerpen:	Text classification Machine learning Pre-processing Feature extraction Naïve bayes Decision tree Machine learning. Text processing (Computer science)
Online toegang:	http://hdl.handle.net/10361/11026

id	10361-11026
record_format	dspace
spelling	10361-110262022-01-26T10:04:52Z Text classification using machine learning algorithms Hasnat, Fahim Hasan, Md. Mazidul Khan, Nayeem Hasan Ali, Asif Chakrabarty, Amitabha Department of Computer Science and Engineering, BRAC University Text classification Machine learning Pre-processing Feature extraction Naïve bayes Decision tree Machine learning. Text processing (Computer science) Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-46). This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Financial, educational and communal activities produce enormous amount of data. Automatic text classification has significant application in content organization, point of view extraction, evaluation analysis, spam filtering and sentiment analysis. Automatic classification of text documents requires information extraction, machine learning and Natural Language processing. We have proposed a probabilistic framework for text classification. Proposed classification model is composed of three major modules i.e. pre-processing of unstructured text, learning of probabilistic model and the classification of unseen data by using learned model. This framework is trained and tested by using “20 newsgroup” dataset containing twenty different news categories i.e. politics, sports, religions and pc hardware. We have used both supervised and unsupervised algorithms to get the full insight on the relationships among various text classification techniques. Highest accuracy of 84.51% was achieved for 4 categories by Naïve Bayes among the other Supervised Algorithms we used and 62.79% homogeneity was achieved for unsupervised algorithms for 4 categories which demonstrates the effectiveness score of proposed automatic text classification approach. Fahim Hasnat Md. Mazidul Hasan Nayeem Hasan Khan Asif Ali B. Computer Science and Engineering 2018-12-18T10:46:31Z 2018-12-18T10:46:31Z 2018 8/2/2018 Thesis ID 14101043 ID 14301104 ID 14301113 ID 12201068 http://hdl.handle.net/10361/11026 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. 46 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Text classification Machine learning Pre-processing Feature extraction Naïve bayes Decision tree Machine learning. Text processing (Computer science)
spellingShingle	Text classification Machine learning Pre-processing Feature extraction Naïve bayes Decision tree Machine learning. Text processing (Computer science) Hasnat, Fahim Hasan, Md. Mazidul Khan, Nayeem Hasan Ali, Asif Text classification using machine learning algorithms
description	Cataloged from PDF version of thesis.
author2	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Hasnat, Fahim Hasan, Md. Mazidul Khan, Nayeem Hasan Ali, Asif
format	Thesis
author	Hasnat, Fahim Hasan, Md. Mazidul Khan, Nayeem Hasan Ali, Asif
author_sort	Hasnat, Fahim
title	Text classification using machine learning algorithms
title_short	Text classification using machine learning algorithms
title_full	Text classification using machine learning algorithms
title_fullStr	Text classification using machine learning algorithms
title_full_unstemmed	Text classification using machine learning algorithms
title_sort	text classification using machine learning algorithms
publisher	BRAC University
publishDate	2018
url	http://hdl.handle.net/10361/11026
work_keys_str_mv	AT hasnatfahim textclassificationusingmachinelearningalgorithms AT hasanmdmazidul textclassificationusingmachinelearningalgorithms AT khannayeemhasan textclassificationusingmachinelearningalgorithms AT aliasif textclassificationusingmachinelearningalgorithms
_version_	1814306914018787328

Text classification using machine learning algorithms

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