Multi-Label Arabic Text Classification Based On Deep Learning

Abstract

Multi-label text classification is a natural extension of text classification in which each document can be assigned with a possible widespread set of labels. Natural Language Processing (NLP) helps to understand and manipulate text in natural language by using the computer. Arabic Text Classification is challenging recently because the Arabic language is under-resourced although it has many users. The aim of this paper is to build a model to classify Arabic news and help users get and display the most relevant news to their interests. In this paper, we demonstrate the efficiency of using deep learning models in solving Arabic multi-label text classification problem. Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) have been used; we build two models via python. All data has been cleaned to improve the quality of experimental data. The result of test data in LSTM was 82.03 whereas in the MLP model was 80.37, and both models were evaluated using F1 score.