Comparative analysis of text classification algorithms for automated labelling of quranic verses
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 l...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Insight - Indonesian Society for Knowledge and Human Development
2017
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| Online Access: | http://eprints.uthm.edu.my/3423/1/AJ%202017%20%28487%29.pdf |
| _version_ | 1825637072462938112 |
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| author | Adeleke, Abdullah Samsudin, Noor Azah Mustapha, Aida Mohd Nawi, Nazri |
| author_facet | Adeleke, Abdullah Samsudin, Noor Azah Mustapha, Aida Mohd Nawi, Nazri |
| author_sort | Adeleke, Abdullah |
| collection | UTHM |
| description | 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 verse using text classification algorithms. We applied three text 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. |
| first_indexed | 2024-03-05T21:45:45Z |
| format | Article |
| id | uthm.eprints-3423 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:45:45Z |
| publishDate | 2017 |
| publisher | Insight - Indonesian Society for Knowledge and Human Development |
| record_format | dspace |
| spelling | uthm.eprints-34232021-11-17T06:23:15Z http://eprints.uthm.edu.my/3423/ Comparative analysis of text classification algorithms for automated labelling of quranic verses Adeleke, Abdullah Samsudin, Noor Azah Mustapha, Aida Mohd Nawi, Nazri QA76 Computer software 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 verse using text classification algorithms. We applied three text 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. Insight - Indonesian Society for Knowledge and Human Development 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/3423/1/AJ%202017%20%28487%29.pdf Adeleke, Abdullah and Samsudin, Noor Azah and Mustapha, Aida and Mohd Nawi, Nazri (2017) Comparative analysis of text classification algorithms for automated labelling of quranic verses. International Journal on Advanced Science Engineering Information Technology, 7 (4). pp. 1419-1427. ISSN 2088-5334 https://dx.doi.org/10.18517/ijaseit.7.4.2198 |
| spellingShingle | QA76 Computer software Adeleke, Abdullah Samsudin, Noor Azah Mustapha, Aida Mohd Nawi, Nazri Comparative analysis of text classification algorithms for automated labelling of quranic verses |
| title | Comparative analysis of text classification algorithms for automated labelling of quranic verses |
| title_full | Comparative analysis of text classification algorithms for automated labelling of quranic verses |
| title_fullStr | Comparative analysis of text classification algorithms for automated labelling of quranic verses |
| title_full_unstemmed | Comparative analysis of text classification algorithms for automated labelling of quranic verses |
| title_short | Comparative analysis of text classification algorithms for automated labelling of quranic verses |
| title_sort | comparative analysis of text classification algorithms for automated labelling of quranic verses |
| topic | QA76 Computer software |
| url | http://eprints.uthm.edu.my/3423/1/AJ%202017%20%28487%29.pdf |
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