Extended Topical Classification of Hadith Arabic Text
Mohammed Naji Al-Kabi, Heider A. Wahsheh, Izzat Mahmoud Alsmadi, Abdallah Moh'd Ali Al-Akhras · 2015
Abstract Applications of automatic text classification to one of many possible categories extend frequently in many information system and domain areas. Research on different methods of text classification evaluates the quality of this classification in terms of performance or accuracy metrics. In this paper we evaluated the automatic classification of Islam prophet sayings based on several possible categories. This study aims to evaluate the effectiveness of four well-known classification algorithms (Naive Bayes ( NB ), Bagging, Support Vector Machine ( SVM and LogiBoost) to classify Prophet Mohammed (Peace and blessings of Allah be upon him (PBUH)) Arabic text sayings into one of five classes (books) ((Ablutions (Wudu'), Fasting, Almsgiving (Zakat), Prayers, and Call to Prayers (Adhaan))). This study is based on (Sahih Bukhari, صحيح البخاري ) collection that is considered the first of (The Two Authentic, الصحيحين ) collections. Evaluation results showed that ( NB algorithm is more effective than the other three classification algorithms used in this study.