Arabic Sentiment Analysis based on Deep Reinforcement Learning

Mohamed Zouidine, Mohammed Khalil
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Abstract

In this work, we handle the problem of Arabic sentiment analysis by combining the Arabic language understanding transformer-based model AraBERT and an LSTM-CNN deep learning model. We propose a new training objective function based on deep reinforcement learning that combines cross-entropy loss from maximum likelihood estimation and rewards from policy gradient algorithm. We evaluate our proposed system on the LABR book reviews dataset. Experimental results show that the proposed model outperforms the state-of-the-art models and provides an accuracy of 87.58%.
基于深度强化学习的阿拉伯语情感分析
在这项工作中,我们通过结合基于阿拉伯语理解转换器的模型AraBERT和LSTM-CNN深度学习模型来处理阿拉伯语情感分析问题。我们提出了一种新的基于深度强化学习的训练目标函数,它结合了最大似然估计的交叉熵损失和策略梯度算法的奖励。我们在LABR书评数据集上评估了我们提出的系统。实验结果表明,该模型优于现有模型,准确率达到87.58%。
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