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Comparative Analysis of Text Classification Algorithms for Automated Labelling of Quranic Verses.

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@article{IJASEIT2198,
   author = {Abdullahi Oyekunle Adeleke and Noor Azah Samsudin and Aida Mustapha and Nazri M Nawi},
   title = {Comparative Analysis of Text Classification Algorithms for Automated Labelling of Quranic Verses.},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {4},
   year = {2017},
   pages = {1419--1427},
   keywords = {Holy Quran; Feature Selection Techniques; k-Nearest Neighbour; Support Vector Machine; Naïve Bayes},
   abstract = {The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verses using text classification algorithms. We applied three classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as "Shahadah" (the first pillar of Islam) or "Pray" (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2198},
   doi = {10.18517/ijaseit.7.4.2198}
}

EndNote

%A Adeleke, Abdullahi Oyekunle
%A Samsudin, Noor Azah
%A Mustapha, Aida
%A Nawi, Nazri M
%D 2017
%T Comparative Analysis of Text Classification Algorithms for Automated Labelling of Quranic Verses.
%B 2017
%9 Holy Quran; Feature Selection Techniques; k-Nearest Neighbour; Support Vector Machine; Naïve Bayes
%! Comparative Analysis of Text Classification Algorithms for Automated Labelling of Quranic Verses.
%K Holy Quran; Feature Selection Techniques; k-Nearest Neighbour; Support Vector Machine; Naïve Bayes
%X The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verses using text classification algorithms. We applied three classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as "Shahadah" (the first pillar of Islam) or "Pray" (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2198
%R doi:10.18517/ijaseit.7.4.2198
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 4
%@ 2088-5334

IEEE

Abdullahi Oyekunle Adeleke,Noor Azah Samsudin,Aida Mustapha and Nazri M Nawi,"Comparative Analysis of Text Classification Algorithms for Automated Labelling of Quranic Verses.," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, pp. 1419-1427, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.4.2198.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Adeleke, Abdullahi Oyekunle
AU  - Samsudin, Noor Azah
AU  - Mustapha, Aida
AU  - Nawi, Nazri M
PY  - 2017
TI  - Comparative Analysis of Text Classification Algorithms for Automated Labelling of Quranic Verses.
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4
Y2  - 2017
SP  - 1419
EP  - 1427
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Holy Quran; Feature Selection Techniques; k-Nearest Neighbour; Support Vector Machine; Naïve Bayes
N2  - The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verses using text classification algorithms. We applied three classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as "Shahadah" (the first pillar of Islam) or "Pray" (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2198
DO  - 10.18517/ijaseit.7.4.2198

RefWorks

RT Journal Article
ID 2198
A1 Adeleke, Abdullahi Oyekunle
A1 Samsudin, Noor Azah
A1 Mustapha, Aida
A1 Nawi, Nazri M
T1 Comparative Analysis of Text Classification Algorithms for Automated Labelling of Quranic Verses.
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 4
YR 2017
SP 1419
OP 1427
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Holy Quran; Feature Selection Techniques; k-Nearest Neighbour; Support Vector Machine; Naïve Bayes
AB The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verses using text classification algorithms. We applied three classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as "Shahadah" (the first pillar of Islam) or "Pray" (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2198
DO  - 10.18517/ijaseit.7.4.2198