Abstract
In this study, we investigate various deep learning models based on convolutional neural networks (CNNs) and Long Short Term Memory (LSTM) recurrent neural networks for sentiment analysis of Arabic microblogs. Unlike English, the Arabic language has several specifics which complicate the process of feature extraction by traditional methods. We adopted a neural language model created at Google, known as word2vec, for vectorizing text. We then designed and evaluated several deep learning architectures using CNN and LSTM. The experiments were run on two publicly available Arabic tweets datasets. Promising results have been attained when combining LSTMs and compared favorably with most related work.
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Acknowledgments
The authors would like to acknowledge the support provided by the Deanship of Scientific Research at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, during this work.
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Al-Azani, S., El-Alfy, ES.M. (2017). Hybrid Deep Learning for Sentiment Polarity Determination of Arabic Microblogs. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_51
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