Al-hadith text classifier.

Mohammed Naji Al-Kabi; Ghassan Kanaan; Ronza S.  Al-Mustafa; Riyad Al-Shalabi; Saja I.  Al-Sinjilawi

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Al-hadith text classifier.

Author(s)

Mohammed Naji Al-Kabi Department of Computer Information Systems, Yarmouk University, P.O. BOX 566, 21163 Irbid, Jordan

Ghassan Kanaan Department of Computer Information Systems, Yarmouk University, P.O. BOX 566, 21163 Irbid, Jordan

Ronza S. Al-Mustafa Department of Computer Information Systems, Yarmouk University, P.O. BOX 566, 21163 Irbid, Jordan

Riyad Al-Shalabi Adress missing

Saja I. Al-Sinjilawi Department of Computer Science, Yarmouk University, P.O. BOX 566, 21163 Irbid, Jordan

Abstract

This study explore the implementation of a text classification method to classify the prophet Mohammad (PBUH) hadiths (sayings) using Sahih Al-Bukhari classification. The sayings explain the Holy Qur’an, which considered by Muslims to be the direct word of Allah. Present method adopts TF/IDF (Term Frequency-Inverse Document Frequency) which is used usually for text search. TF/IDF was used for term weighting, in which document weights for the selected terms are computed, to classify non-vocalized sayings, after their terms (keywords have been transformed to the corresponding canonical form (i.e., roots), to one of eight Books (classes), according to Al-Bukhari classification. A term would have a higher weight if it were a good descriptor for a particular book, i.e., it appears frequently in the book but is infrequent in the entire corpus. The classifier first uses a training set as a learning phase and then uses the test set to evaluate the accuracy of this classifier; the average accuracy for this sample is approximately 83.2%.

Publication Details

Page(s) 584-587

DOI DOI not available

Published Journal: Journal of Applied Sciences, Volume: 5, Issue: 3, Year: 2005

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