A Framework for Arabic Tweets Multi-label Classification Using Word Embedding and Neural Networks Algorithms
Abstract
The need for classifying tweets is essential for many people like
tourists, tourism companies and governments. In this paper, we
propose a framework for Arabic Tweets multi-label classification
using word embedding technique and deep leering algorithms. We
built our dataset using 160k Arabic tweets gathered from Twitter.
We compared two deep learning methods, Convolutional Neural
Networks (CNN) and Recurrent Neural Networks (RNN). Our
results show that it is possible to classify tweets using our
methodology without any significant difference in results of
accuracy scores and hamming loss for both types of networks. The
accuracy scores and hamming loss were nearly 90% and 0.02,
respectively.