%0 Journal Article
%T Al-Hadith Text Classifier
%A Mohammed Naji Al-Kabi
%A Ghassan Kanaan
%A Riyad Al-Shalabi
%A Saja I. Al- Sinjilawi
%J Journal of Applied Sciences
%D 2005
%I Asian Network for Scientific Information
%X 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.
%K statistical analysis
%K Arabic Text categorization
%K hadith (prophet sayings) text classifier
%K Arabic text classification
%K Arabic text mining
%K data-mining
%K classification
%U http://docsdrive.com/pdfs/ansinet/jas/2005/584-587.pdf