Journal
2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)
Volume -, Issue -, Pages 1471-1474Publisher
IEEE
DOI: 10.1109/ICMLA.2018.00239
Keywords
sentiment analysis; natural language processing; deep learning; long-short term memory
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Funding
- Science & Technology Center: Bio/Computational Evolution in Action Consortium (BEACON)
- National Science Foundation
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Artificial Intelligence (AI) has been used widely to extract people's opinions from social media websites. However, most of the existing works focus on eliciting the features from English text. In this paper, we describe an Arabic text sentiment analysis approach using a Deep Neural network, namely Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). In this research, we investigate how the different pre-trained Word Embedding (WE) models affect our model's accuracy. The dataset includes Arabic corpus collected from Twitter. The results show significant improvement in Arabic text classification.
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