Arabic text classification using deep learning technics

Abstract

Text classification is the process of gathering documents into classes and categories based on their contents. This process is becoming more important due to the huge textual information available online. The main problem in text classification is how to improve the classification accuracy. Many algorithms have been proposed and implemented to solve this problem in general. However, few studies have been carried out for categorizing and classifying Arabic text. Technically, the process of text classification follows two steps; the first step consists on selecting some special features from all the features available from the text by applying features selection, features reduction and features weighting techniques. And the second step applies classification algorithms on those chosen features. In this paper, we present an innovative method for Arabic text classification. We use an Arabic stemming algorithm to extract, select and reduce the features that we need. After that, we use the Term Frequency-Inverse Document Frequency technique as feature weighting technique. And finally, for the classification step, we use one of the deep learning algorithms that is very powerful in other field such as the image processing and pattern recognition, but still rarely used in text mining, this algorithm is the Convolutional Neural Networks. With this combination and some hyperparameter tuning in the Convolutional Neural Networks algorithm we can achieve excellent results on multiple benchmarks.