Topic identification using filtering and rule generation algorithm for textual document

Information stored digitally in text documents are seldom arranged according to specific topics. The necessity to read whole documents is time-consuming and decreases the interest for searching information. Most existing topic identification methods depend on occurrence of terms in the text. Howev...

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Main Author:	Nurul Syafidah, Jamil
Format:	Thesis
Language:	eng eng
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	https://etd.uum.edu.my/5379/1/s812431.pdf https://etd.uum.edu.my/5379/2/s812431_abstract.pdf
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id	my-uum-etd.5379
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ku Mahamud, Ku Ruhana Mohamed Din, Aniza
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Nurul Syafidah, Jamil Topic identification using filtering and rule generation algorithm for textual document
description	Information stored digitally in text documents are seldom arranged according to specific topics. The necessity to read whole documents is time-consuming and decreases the interest for searching information. Most existing topic identification methods depend on occurrence of terms in the text. However, not all frequent occurrence terms are relevant. The term extraction phase in topic identification method has resulted in extracted terms that might have similar meaning which is known as synonymy problem. Filtering and rule generation algorithms are introduced in this study to identify topic in textual documents. The proposed filtering algorithm (PFA) will extract the most relevant terms from text and solve synonym roblem amongst the extracted terms. The rule generation algorithm (TopId) is proposed to identify topic for each verse based on the extracted terms. The PFA will process and filter each sentence based on nouns and predefined keywords to produce suitable terms for the topic. Rules are then generated from the extracted terms using the rule-based classifier. An experimental design was performed on 224 English translated Quran verses which are related to female issues. Topics identified by both TopId and Rough Set technique were compared and later verified by experts. PFA has successfully extracted more relevant terms compared to other filtering techniques. TopId has identified topics that are closer to the topics from experts with an accuracy of 70%. The proposed algorithms were able to extract relevant terms without losing important terms and identify topic in the verse.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Nurul Syafidah, Jamil
author_facet	Nurul Syafidah, Jamil
author_sort	Nurul Syafidah, Jamil
title	Topic identification using filtering and rule generation algorithm for textual document
title_short	Topic identification using filtering and rule generation algorithm for textual document
title_full	Topic identification using filtering and rule generation algorithm for textual document
title_fullStr	Topic identification using filtering and rule generation algorithm for textual document
title_full_unstemmed	Topic identification using filtering and rule generation algorithm for textual document
title_sort	topic identification using filtering and rule generation algorithm for textual document
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2015
url	https://etd.uum.edu.my/5379/1/s812431.pdf https://etd.uum.edu.my/5379/2/s812431_abstract.pdf
_version_	1747827921170464768
spelling	my-uum-etd.53792021-04-04T08:54:11Z Topic identification using filtering and rule generation algorithm for textual document 2015 Nurul Syafidah, Jamil Ku Mahamud, Ku Ruhana Mohamed Din, Aniza Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA75 Electronic computers. Computer science Information stored digitally in text documents are seldom arranged according to specific topics. The necessity to read whole documents is time-consuming and decreases the interest for searching information. Most existing topic identification methods depend on occurrence of terms in the text. However, not all frequent occurrence terms are relevant. The term extraction phase in topic identification method has resulted in extracted terms that might have similar meaning which is known as synonymy problem. Filtering and rule generation algorithms are introduced in this study to identify topic in textual documents. The proposed filtering algorithm (PFA) will extract the most relevant terms from text and solve synonym roblem amongst the extracted terms. The rule generation algorithm (TopId) is proposed to identify topic for each verse based on the extracted terms. The PFA will process and filter each sentence based on nouns and predefined keywords to produce suitable terms for the topic. Rules are then generated from the extracted terms using the rule-based classifier. An experimental design was performed on 224 English translated Quran verses which are related to female issues. Topics identified by both TopId and Rough Set technique were compared and later verified by experts. PFA has successfully extracted more relevant terms compared to other filtering techniques. TopId has identified topics that are closer to the topics from experts with an accuracy of 70%. The proposed algorithms were able to extract relevant terms without losing important terms and identify topic in the verse. 2015 Thesis https://etd.uum.edu.my/5379/ https://etd.uum.edu.my/5379/1/s812431.pdf text eng public https://etd.uum.edu.my/5379/2/s812431_abstract.pdf text eng public masters masters Universiti Utara Malaysia Abdullah, Z., Kassim, J. M., & Saad, N. (2009). Pembangunan Perpustakaan Digital: Ayat al-Quran Berkaitan Wanita. Asia-Pacific Journal of Information Technology and Multimedia, 6(1). Abdullah, A.H.H., & Sudiro, S.R. (2010). Wanita menurut Hamka di dalam Tafsir Al-Azhar: Kajian terhadap Surah An-Nisa’. Aery, M., Ramamurthy, N., & Aslandogan, Y. A. (2003). Topic identification of textual data. Technical report, The University of Texas at Arlington. Aggarwal, C.C., & Zhai, C.X. (2012). A survey of text classification algorithm. Ahmad, O., Hyder, I., Iqbal, R., Murad, M. A. A., Mustapha, A., Sharef, N. M., & Mansoor, M. (2013). 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