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Improving Sentiment Analysis in Arabic and English Languages by Using Multi-Layer Perceptron Model (MLP)

机译:通过使用多层感知器模型(MLP)改进阿拉伯语和英语的情感分析

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Sentiment analysis is the process of analyzing people’s opinion and feelings toward individual, entities, issues, items or topics. Within the last couple of years, this field has gained increasing research interest varying from simple linear models to more complex neural networks models. Several challenges including the grammar, structures of the language and Morphology which one-word lead to many important meanings. The varieties of dialects and the lack of the appropriate corpora limit the use of sentiment analysis on Arabic content. Throughout this paper, we aim at providing a review on the utilization of the deep learning (DL) approach to analyze sentiments expressed in the Arabic text by using Multilayer perceptron (MLP) model. The results show that the MLP model shows highly effective performance in sentiment analysis for both Arabic and English languages
机译:情感分析是分析人们对个人,实体,问题,项目或主题的看法和感受的过程。在过去的几年中,该领域的研究兴趣不断增加,从简单的线性模型到更复杂的神经网络模型,不一而足。几个挑战,包括语法,语言的结构和词法,其中一个词会带来许多重要的意义。方言的多样性和缺乏适当的语料库限制了对阿拉伯语内容进行情感分析的使用。在整个本文中,我们旨在提供有关使用深度学习(DL)方法通过使用多层感知器(MLP)模型来分析阿拉伯语文本中表达的情绪的综述。结果表明,MLP模型在阿拉伯语和英语的情感分析中显示出非常有效的表现

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