Automatic Arabic Document Classification Based on the HRWiTD Algorithm

Abstract

The documents contain a large amount of valuable knowledge on various subjects and, more recently, documents on the Internet are available from various sources. Therefore, automatic, rapid and accurate classification of these documents with less human interaction has become necessary. In this paper, we introduce a new algorithm called the highest repetition of words in a text document (HRWiTD) to classify the automatic Arabic text. The corpus is divided into a train set and a test set to be applied to proposed classification technique. The train set is analyzed for learning and the learning data is stored in the Learning Dataset file. The category that contains the highest repetition for each word is assigned as a category for the word in Learning Dataset file. This file includes non-duplicate words with the value of higher repetition and categories and they get from all texts in the train set. For each text in the test set, the category of words is assigned to a specific category by using Learning Dataset file. The category that contains the largest number of words is assigned as the predicted category of the text. To evaluate the classification accuracy of the HRWiTD algorithm, the confusion matrix method is used. The HRWiTD algorithm has been applied to convergent samples from six categories of Arabic news at SPA (Saudi Press Agency). As a result, the accuracy of the HRWiTD algorithm is 86.84%. In addition, we used the same corpus with the most popular machine learning algorithms which are C5.0, KNN, SVM, NB and C4.5, and their results of classification accuracy are 52.86%, 52.38%, 51.90%, 51.90% and 30%, respectively. Thus, the HRWiTD algorithm gives better classification accuracy compared to the most popular machine learning algorithms on the selected domain.