Abstract
Background: A hadith refers to sayings, actions, and characteristics of the Prophet Muhammad peace be upon him. The authenticity of hadiths is crucial, because they constitute the source of legislation for Muslims with the Holy Quran. Classifying hadiths into groups is a matter of importance as well, to make them easy to search and recognize.
Objective: To report the results of a systematic review concerning hadith authentication and classification methods.
Data sources: Original articles found in ACM, IEEE Xplore, ScienceDirect, Scopus, Web of Science, Springer Link, and Wiley Online Library.
Study selection criteria: Only original articles written in English and dealing with hadith authentication and classification. Reviews, editorial, letters, grey literature, and restricted or incomplete articles are excluded.
Data extraction: Two authors were assigned to extract data using a predefined data extraction form to answer research questions and assess studies quality.
Results: A total of 27 studies were included in this review. There are 14 studies in authentication and 13 studies in classification. Most of the selected studies (17 of 27) were published in conferences, while the others (10 of 27) were published in scientific journals. Research in the area of hadith authentication and classification has received more attention in recent years (2016–2019).
Conclusions: Hadith authentication methods are classified into machine learning, rule-based, and a hybrid of rule-based and machine learning and rule-based and statistical methods. Hadith classification methods are classified into machine learning and rule-based. All classification studies used Matn, while the majority of authentication studies used isnad. As a dataset source, Sahih Al-Bukhari was used by most studies. None of the used datasets is publicly available as a benchmark dataset, either in hadith authentication or classification. Recall and Precision are the most frequent evaluation metrics used by the selected studies.
- Hammam M. Abdelaal, Berihan R. Elemary, and Hassan A. Youness. 2019. Classification of hadith according to its content based on supervised learning algorithms. IEEE Access 7 (2019), 152379–152387.Google Scholar
Cross Ref
- Hammam M. Abdelaal and Hassan A. Youness. 2019. Hadith classification using machine learning techniques according to its reliability. Roman. J. Info. Sci. Technol. 22, 3–4 (2019), 259–271.Google Scholar
- Muhammad Fauzan Afianto, Said Al-Faraby et al. 2018. Text categorization on hadith Sahih Al-Bukhari using random forest. In Journal of Physics: Conference Series, Vol. 971. IOP Publishing, 012037.Google Scholar
- Said Al Faraby, Eliza Riviera Rachmawati Jasin, Andina Kusumaningrum et al. 2018. Classification of hadith into positive suggestion, negative suggestion, and information. In Journal of Physics: Conference Series, Vol. 971. IOP Publishing, 012046.Google Scholar
- Mohammed Naji Al-Kabi, Ghassan Kanaan, Riyad Al-Shalabi, Saja I. Al-Sinjilawi, and Ronza S. Al-Mustafa. 2005. Al-Hadith text classifier. J. Appl. Sci. 5, 3 (2005), 584–587.Google Scholar
Cross Ref
- Mohammed N. Al-Kabi, Heider A. Wahsheh, and Izzat M. Alsmadi. 2014. A topical classification of hadith Arabic text. Proceedings of the 2nd International Conference on Islamic Applications in Computer Science and Technology (IMAN’14).Google Scholar
- Mohammed N. Al-Kabi, Heider A. Wahsheh, Izzat M. Alsmadi, and A. M. A. Al-Akhras. 2015. Extended topical classification of hadith Arabic text. Int. J. Islam Appl. Comput. Sci. Technol. 3, 3 (2015), 13–23.Google Scholar
- Kawther A. Aldhlan, Akram M. Zeki, Ahmad M. Zeki, and Hamad A. Alreshidi. 2012. Novel mechanism to improve hadith classifier performance. In Proceedings of the International Conference on Advanced Computer Science Applications and Technologies (ACSAT’12). IEEE, 512–517. Google Scholar
Digital Library
- Manar Alkhatib. 2010. Classification of Al-Hadith Al-Shareef using data mining algorithm. In Proceedings of the European, Mediterranean, and Middle Eastern Conference on Information Systems (EMCIS’10). 1–23.Google Scholar
- Manar Alkhatib, Azza Abdel Monem, and Khaled Shaalan. 2017. A rich Arabic WordNet resource for Al-Hadith Al-Shareef. Procedia Comput. Sci. 117 (2017), 101–110.Google Scholar
Cross Ref
- Aqil M. Azmi, Abdulaziz O. Al-Qabbany, and Amir Hussain. 2019. Computational and natural language processing based studies of hadith literature: A survey. Artific. Intell. Rev. (2019), 1–46. Google Scholar
Digital Library
- Aqil M. Azmi and Amjad M. AlOfaidly. 2014. A novel method to automatically pass hukm on Hadith. In Proceedings of the 5th International Conference on Arabic Language Processing (CITALA’14). 118–124.Google Scholar
- Muhammad Yuslan Abu Bakar, Said Al Faraby et al. 2018. Multi-label topic classification of hadith of Bukhari (Indonesian language translation) using information gain and backpropagation neural network. In Proceedings of the International Conference on Asian Language Processing (IALP’18). IEEE, 344–350.Google Scholar
Cross Ref
- Soad Saleh Balgasem and Lailatul Qadri Zakaria. 2017. A hybrid method of rule-based approach and statistical measures for recognizing narrators name in hadith. In Proceedings of the 6th International Conference on Electrical Engineering and Informatics (ICEEI’17). IEEE, 1–5.Google Scholar
Cross Ref
- Kashif Bilal and Sajjad Mohsin. 2012. Muhadith: A cloud based distributed expert system for classification of ahadith. In Proceedings of the 10th International Conference on Frontiers of Information Technology. IEEE, 73–78. Google Scholar
Digital Library
- Ibrahim Bounhas. 2019. On the usage of a classical Arabic corpus as a language resource: Related research and key challenges. ACM Trans. Asian Low-Res. Lang. Info. Process. 18, 3 (2019), 23. Google Scholar
Digital Library
- CASP. 2019. Retrieved from https://casp-uk.net/wp-content/uploads/2018/01/CASP-Qualitative-Checklist-2018.pdf. Google Scholar
- Krzysztof Dembczyński, Willem Waegeman, Weiwei Cheng, and Eyke Hüllermeier. 2010. Regret analysis for performance metrics in multi-label classification: The case of hamming and subset zero-one loss. In Machine Learning and Knowledge Discovery in Databases, J. L. Balcázar, F. Bonchi, A. Gionis, M. Sebag (Eds). ECML PKDD 2010. Lecture Notes in Computer Science, vol 6321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15880-3_24 Google Scholar
Digital Library
- Tore Dybå and Torgeir Dingsøyr. 2008. Empirical studies of agile software development: A systematic review. Info. Softw. Technol. 50, 9-10 (2008), 833–859. Google Scholar
Digital Library
- Mohamed Ghanem, Abdelaaziz Mouloudi, and Mohammed Mourchid. 2016. Classification of hadiths using LVQ based on VSM considering words order. Int. J. Comput. Appl. 148, 4 (2016).Google Scholar
- Mehdi Ghazizadeh, M. Hadi Zahedi, Mohsen Kahani, and B. Minaei Bidgoli. 2008. Fuzzy expert system in determining hadith 1 validity. In Advances in Computer and Information Sciences and Engineering. Springer, 354–359.Google Scholar
- Fouzi Harrag and Eyas El-Qawasmah. 2009. Neural network for arabic text classification. In Proceedings of the 2nd International Conference on the Applications of Digital Information and Web Technologies. IEEE, 778–783.Google Scholar
Cross Ref
- Abdelaali Hassaine, Zeineb Safi, and Ali Jaoua. 2016. Authenticity detection as a binary text categorization problem: Application to Hadith authentication. In Proceedings of the IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA’16). IEEE, 1–7.Google Scholar
Cross Ref
- Mohammad Hossin and M. N. Sulaiman. 2015. A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manage. Process 5, 2 (2015), 1.Google Scholar
Cross Ref
- Brian Hutton, Georgia Salanti, Deborah M. Caldwell, Anna Chaimani, Christopher H. Schmid, Chris Cameron, John P. A. Ioannidis, Sharon Straus, Kristian Thorlund, Jeroen P. Jansen, et al. 2015. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: Checklist and explanations. Ann. Internal Med. 162, 11 (2015), 777–784.Google Scholar
Cross Ref
- Nuzulha Khilwani Ibrahim, Suhaila Samsuri, Muhamad Sadry Abu Seman, Ahmed Elmogtaba Banga Ali, and Mira Kartiwi. 2016. Frameworks for a computational isnad authentication and mechanism development. In Proceedings of the 6th International Conference on Information and Communication Technology for The Muslim World (ICT4M’16). IEEE, 154–159.Google Scholar
Cross Ref
- Khitam Jbara. 2010. Knowledge discovery in Al-Hadith using text classification algorithm. J. Amer. Sci. 6, 11 (2010), 409–419.Google Scholar
- Muhammad Nomani Kabir, Md Munirul Hasan, Md Arafatur Rahman, and Hai Tao. 2018. Development of a web-extension for authentication of online Hadith texts. Int. J. Eng. Technol. 7, 2. 5 (2018), 19–22.Google Scholar
- Muhammad Nomani Kabir, Omar Tayan, Yasser Alginahi, Md Munirul Hasan, and Md Arafatur Rahman. 2019. On the development of a web extension for text authentication on Google Chrome. In Proceedings of the International Conference on Electrical, Computer and Communication Engineering (ECCE’19). IEEE, 1–5.Google Scholar
Cross Ref
- Staffs Keele et al. 2007. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report. Technical report, Ver. 2.3 EBSE Technical Report. EBSE.Google Scholar
- Muhammad Muhsin Khan. 1986. Sahih Bukhari. Vol. 6. Peace Vision.Google Scholar
- Anders Kofod-Petersen. 2012. How to do a structured literature review in computer science. Ver. 0.1 (Oct. 2012).Google Scholar
- Emha Taufiq Luthfi, Nanna Suryana, and Abdulsamad Hasan Basari. 2018. Digital hadith authentication: A literature review and analysis. J. Theoret. Appl. Info. Technol. 96, 15 (2018).Google Scholar
- Gugun Mediamer, Adiwijaya, and Said Al Faraby. 2019. Development of rule-based feature extraction in multi-label text classification. Int. J. Adv. Sci. Eng. Info. Technol. 9, 4 (2019), 1460–1465.Google Scholar
Cross Ref
- Manal Mohammed and Nazlia Omar. 2018. Question classification based on Bloom’s Taxonomy using enhanced TF-IDF. Int. J. Adv. Sci. Eng. Info. Technol. 8, 4–2 (2018), 1679–1685.Google Scholar
- Moath M. Najeeb. 2014. Towards innovative system for Hadith Isnad processing. Int. J. Comput. Trends Technol. 18, 6 (2014), 257–259.Google Scholar
Cross Ref
- Ina Najiyah, Sari Susanti, Dwiza Riana, and Mochammad Wahyudi. 2017. Hadith degree classification for Shahih Hadith identification web based. In Proceedings of the 5th International Conference on Cyber and IT Service Management (CITSM’17). IEEE, 1–6.Google Scholar
Cross Ref
- Nur Aqilah Paskhal Rostam and Nurul Hashimah Ahamed Hassain Malim. 2019. Text categorisation in Quran and Hadith: Overcoming the interrelation challenges using machine learning and term weighting. J. King Saud Univ.-Comput. Info. Sci. (2019). In Press.Google Scholar
- Mohammad Arshi Saloot, Norisma Idris, Rohana Mahmud, Salinah Ja’afar, Dirk Thorleuchter, and Abdullah Gani. 2016. Hadith data mining and classification: A comparative analysis. Artific. Intell. Rev. 46, 1 (2016), 113–128. Google Scholar
Digital Library
- Mohammed Q. Shatnawi, Qusai Q. Abuein, and Omar Darwish. 2011. Verification hadith correctness in islamic web pages using information retrieval techniques. In Proceedings of International Conference on Information and Communication Systems. Citeseer, 164–167.Google Scholar
- Marina Sokolova and Guy Lapalme. 2009. A systematic analysis of performance measures for classification tasks. Info. Process. Manage. 45, 4 (2009), 427–437. Google Scholar
Digital Library
Index Terms
A Systematic Review on Hadith Authentication and Classification Methods
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