Multi-channel Embedding Convolutional Neural Network Model for Arabic Sentiment Classification

In this paper, a multi-channel embedding convolutional neural network (MCE-CNN) is proposed to improve Arabic sentiment classification by learning sentiment features from different text domains, word and character n-grams levels. MCE-CNN encodes a combination of different pre-trained word embeddings into the embedding block at each embedding channel and trains these channels in parallel. Besides, a separate feature extraction module implemented in a CNN block is used to extract more relevant sentiment features. These channels and blocks help to start training on high-quality WEVs and fine-tuning them. The performance of MCE-CNN is evaluated on several standard balanced and imbalanced datasets to reflect real-world using cases. Experimental results show that MCE-CNN provides a high classification accuracy and benefits from the second embedding channel on both standard Arabic and dialectal Arabic text, which outperforms state-of-the-art methods.

Recommended citation: Dahou, A., Xiong, S., Zhou, J., & Mohamed, Abd Elaziz. “Multi-channel Embedding Convolutional Neural Network Model for Arabic Sentiment Classification.” (TALLIP 2019).