Sentiment Analysis of Arabic Tweets using Deep Learning

Abstract

Sentiment analysis is the computational study of people's opinions, attitudes and emotions toward entities, individuals, issues, events or topics. A lot of research has been done to improve the accuracy of sentiment analysis, varying from simple linear models to more complex deep neural network models. Recently, deep learning has shown great success in the field of sentiment analysis and is considered as the state-of-the-art model in various languages. However, the state-of-the-art accuracy for Arabic sentiment analysis still needs improvements. The Arabic language imposes many challenges, due to its complex structure, various dialects, in addition to the lack of its resources. Although the recent deep learning model has improved the accuracy of the Arabic sentiment analysis, there is still more room for improvement. This encouraged us to explore different deep learning models that have not been applied to Arabic data, in order to improve the Arabic sentiment analysis accuracy. In this paper, we used an ensemble model, combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models, to predict the sentiment of Arabic tweets. Our model achieves an F1-score of 64.46%, which outperforms the state-of-the-art deep learning model's F1-score of 53.6%, on the Arabic Sentiment Tweets Dataset (ASTD).