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Journal of Applied Sciences
  Year: 2005 | Volume: 5 | Issue: 3 | Page No.: 584-587
DOI: 10.3923/jas.2005.584.587
 
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Al-Hadith Text Classifier

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

Abstract:
This study explore the implementation of a text classification method to classify the prophet Mohammed (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 set as a learning phase and then uses the best set to evaluate the accuracy of this classifier, the average accuracy for this sample is approximately 83.2%.
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How to cite this article:

Mohammed Naji Al- Kabi, Ghassan Kanaan, Riyad Al- Shalabi, Saja I. Al- Sinjilawi and Ronza S. Al- Mustafa, 2005. Al-Hadith Text Classifier. Journal of Applied Sciences, 5: 584-587.

DOI: 10.3923/jas.2005.584.587

URL: https://scialert.net/abstract/?doi=jas.2005.584.587

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