Al-hadith text classifier. | [Journal of Applied Sciences • 2005]

Author(s):

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

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

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

4. Riyad Al-Shalabi: Adress missing

5. 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%.

Page(s): 584-587

DOI: DOI not available

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

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