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https://ptsldigital.ukm.my/jspui/handle/123456789/476619Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Saidah Saad, Dr. | - |
| dc.contributor.author | Hatem Mohammed Ahmed Saleh Alhasani (P83909) | - |
| dc.date.accessioned | 2023-10-06T09:22:29Z | - |
| dc.date.available | 2023-10-06T09:22:29Z | - |
| dc.date.issued | 2018-11-07 | - |
| dc.identifier.other | ukmvital:122005 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/476619 | - |
| dc.description | The Quran is the primary source of knowledge and guidance for All Muslims. The Qur’an has its own methods of Targhib (Encouragement) and Tarhib (warning), which is an important feature of the Qur’an. Most of the Quranic verses urge and encourage people to do the right and good deeds, also warn them doing the evil and bad deeds. The method of classifying a text into two opposing opinions has been applied previously in solving the problem of sentiment analysis. Now it applied to identify Targhib (encouragement) and Tarhib (warning). Each verse of the Qur’an can be treated as either an encouragement, warning or neutral. The language of the Holy Quran is classical Arabic which is known to be one of the most challenging natural languages in sentiment analysis. The aim of this work is to classify the verses of encouragement and warning using sentiment analysis and NLP techniques. In carrying out this aim, the applied machine learning approach was used, where the impact of the use of different techniques such as POS tagging, N-Gram and Feature selection with correlation based were evaluated and investigated. An annotated corpus from the holy Quran was built which consisted of 2000 verses where 1000 were encouragement verses and the other 1000 were warning verses. 95.6% accuracy was achieved using Naive Bayes when the selected top 5000 features were used. On the other hand, 91.5% accuracy was achieved using the Support Vector Machine when the selected top 5000 features were used. This study is a novel study to extract information and knowledge from the Holy Quran. It is significant for both researchers in the field of Islamic studies as well as non-specialized researchers.,Master of Computer Science,Certification of Master's / Doctoral Thesis" is nat available" | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | - |
| dc.rights | UKM | - |
| dc.subject | Natural language processing (Computer science) | - |
| dc.subject | Computational linguistics | - |
| dc.subject | Qur'an -- Criticism, interpretation, etc. | - |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | - |
| dc.subject | Dissertations, Academic -- Malaysia | - |
| dc.title | Classification of encouragement (targhib) and warning (tarhib) using SVM and NB on Quran | - |
| dc.type | theses | - |
| dc.format.pages | 84 | - |
| dc.identifier.callno | QA76.9.N38H3664 2018 3 tesis | - |
| dc.identifier.barcode | 005529(2021)(PL2) | - |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_122005+SOURCE1+SOURCE1.0.PDF Restricted Access | 17.82 MB | Adobe PDF |  View/Open |
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