Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification
Published in Coling, 2016
In this paper, a scheme of Arabic sentiment classification, which evaluates and detects the sentiment polarity from Arabic reviews and Arabic social media, is studied. We investigated in several architectures to build a quality neural word embeddings using a 3.4 billion words corpus from a collected 10 billion words web-crawled corpus. Moreover, a convolutional neural network trained on top of pre-trained Arabic word embeddings is used for sentiment classification to evaluate the quality of these word embeddings.
Recommended citation: Dahou, A., Xiong, S., Zhou, J., Haddoud, M. H., & Duan, P. “Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification.” (Coling 2016).