Deep learning CNN-LSTM framework for Arabic sentiment analysis using textual information shared in social networks

Recently, the world has witnessed an exponential growth of social networks which have opened a venue for online users to express and share their opinions in different life aspects. Sentiment analysis has become a hot-trend research topic in the field of natural language processing due to its significant roles in analyzing the public's opinion and deriving effective opinion-based decisions. Arabic is one of the widely used languages across social networks. However, its morphological complexities and varieties of dialects make it a challenging language for sentiment analysis. Therefore, inspired by the success of deep learning algorithms, in this paper, we propose a novel deep learning model for Arabic language sentiment analysis based on one layer CNN architecture for local feature extraction, and two layers LSTM to maintain long-term dependencies. The feature maps learned by CNN and LSTM are passed to SVM classifier to generate the final classification. This model is supported by FastText words embedding model. Extensive experiments carried out on a multi-domain corpus demonstrate the outstanding classification performance of this model with an accuracy of 90.75%. Furthermore, the proposed model is validated using different embedding models and classifiers. The results show that FastText skip-gram model and SVM classifier are more valuable alternatives for the Arabic sentiment analysis. The proposed model outperforms several well-established state-of-the-art approaches on relevant corpora with up to + 20.71% accuracy improvement.