Improving Arabic sentiment analysis across context-aware attention deep model based on natural language processing

With the enormous growth of social data in recent years, sentiment analysis has gained increasing research attention and has been widely explored in various languages. Arabic language nature imposes several challenges, such as the complicated morphological structure and the limited resources, Thereby, the current state-of-the-art methods for sentiment analysis remain to be enhanced. This inspired us to explore the application of the emerging deep-learning architecture to Arabic text classification. In this paper, we present an ensemble model which integrates a convolutional neural network, bidirectional long short-term memory (Bi-LSTM), and attention mechanism, to predict the sentiment orientation of Arabic sentences. The convolutional layer is used for feature extraction from the higher-level sentence representations layer, the BiLSTM is integrated to further capture the contextual information from the produced set of features. Two attention mechanism units are incorporated to highlight the critical information from the contextual feature vectors produced by the Bi-LSTM hidden layers. The context-related vectors generated by the attention mechanism layers are then concatenated and passed into a classifier to predict the final label. To disentangle the influence of these components, the proposed model is validated as three variant architectures on a multi-domains corpus, as well as four benchmarks. Experimental results show that incorporating Bi-LSTM and attention mechanism improves the model’s performance while yielding 96.08% in accuracy. Consequently, this architecture consistently outperforms the other State-of-The-Art approaches with up to + 14.47%, + 20.38%, and + 18.45% improvements in accuracy, precision, and recall respectively. These results demonstrated the strengths of this model in addressing the challenges of text classification tasks.