Text Categorization Using Naive Bayes Algorithm

As the volume of information available on the internet and corporate intranet continues to increase, there is a growing interest in helping people better find, filter, and manage all these resources. Text categorization is one of the techniques that can be applied in this situation. This paper prese...

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Main Author: Wan Hazimah, Wan Ismail
Format: Thesis
Language:en
en
Published: 2005
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Online Access:https://etd.uum.edu.my/1368/1/WAN_HAZIMAH_BT._WAN_ISMAIL.pdf
https://etd.uum.edu.my/1368/2/1.WAN_HAZIMAH_BT._WAN_ISMAIL.pdf
https://etd.uum.edu.my/1368/
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author Wan Hazimah, Wan Ismail
author_facet Wan Hazimah, Wan Ismail
author_sort Wan Hazimah, Wan Ismail
building UUM Library
collection Institutional Repository
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
continent Asia
country Malaysia
description As the volume of information available on the internet and corporate intranet continues to increase, there is a growing interest in helping people better find, filter, and manage all these resources. Text categorization is one of the techniques that can be applied in this situation. This paper presents text categorization system based on naive Bayes algorithm. This algorithm has long been used for text categorization tasks. Naive Bayes classifier is based on probability model that integrate strong independence assumptions which often have no bearing in reality. The aims of this project are to categorize the textual document using naive Bayes algorithm and to measure the correctness of the chosen technique for the categorization process. This paper also discusses the experiment in categorizing articles using naive Bayes. The result shows that the accuracy for training is 81.82% whereas the accuracy for testing is 47.62%.
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spelling my.uum.etd-13682013-07-24T12:11:39Z https://etd.uum.edu.my/1368/ Text Categorization Using Naive Bayes Algorithm Wan Hazimah, Wan Ismail QA71-90 Instruments and machines As the volume of information available on the internet and corporate intranet continues to increase, there is a growing interest in helping people better find, filter, and manage all these resources. Text categorization is one of the techniques that can be applied in this situation. This paper presents text categorization system based on naive Bayes algorithm. This algorithm has long been used for text categorization tasks. Naive Bayes classifier is based on probability model that integrate strong independence assumptions which often have no bearing in reality. The aims of this project are to categorize the textual document using naive Bayes algorithm and to measure the correctness of the chosen technique for the categorization process. This paper also discusses the experiment in categorizing articles using naive Bayes. The result shows that the accuracy for training is 81.82% whereas the accuracy for testing is 47.62%. 2005-10-26 Thesis NonPeerReviewed application/pdf en https://etd.uum.edu.my/1368/1/WAN_HAZIMAH_BT._WAN_ISMAIL.pdf application/pdf en https://etd.uum.edu.my/1368/2/1.WAN_HAZIMAH_BT._WAN_ISMAIL.pdf Wan Hazimah, Wan Ismail (2005) Text Categorization Using Naive Bayes Algorithm. Masters thesis, Universiti Utara Malaysia.
spellingShingle QA71-90 Instruments and machines
Wan Hazimah, Wan Ismail
Text Categorization Using Naive Bayes Algorithm
title Text Categorization Using Naive Bayes Algorithm
title_full Text Categorization Using Naive Bayes Algorithm
title_fullStr Text Categorization Using Naive Bayes Algorithm
title_full_unstemmed Text Categorization Using Naive Bayes Algorithm
title_short Text Categorization Using Naive Bayes Algorithm
title_sort text categorization using naive bayes algorithm
topic QA71-90 Instruments and machines
url https://etd.uum.edu.my/1368/1/WAN_HAZIMAH_BT._WAN_ISMAIL.pdf
https://etd.uum.edu.my/1368/2/1.WAN_HAZIMAH_BT._WAN_ISMAIL.pdf
https://etd.uum.edu.my/1368/
url_provider http://etd.uum.edu.my/