Abstract
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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