A Comparative Study of Deep Learning Techniques for Sentiment Analysis of Arabic Text

Abstract

Sentiment analysis of Arabic text has gained significant attention in recent years due to the widespread use of social media and the need to analyze public opinion and sentiment towards various topics. Deep learning techniques have been shown to be effective in achieving high accuracy in sentiment analysis tasks. In this paper, we present a comparative study of deep learning techniques for sentiment analysis of Arabic text, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and their combination. We evaluate the performance of these techniques on a large-scale Arabic sentiment analysis dataset and compare their accuracy, speed, and robustness.