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A Deep Learning Approach to Classify and Quantify the Multiple Emotions of Arabic Tweets

机译:一种深入的学习方法来分类和量化阿拉伯语推文的多种情绪

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In this paper, we introduce both a Multi-Label Classification (MLC) method to determine all the emotions expressed in an Arabic tweet and a Multi-Target Regression (MTR) method to determine the emotions' intensities. MLC involves the prediction of zero or more classes per sample. It is one of the interesting research topics in Natural Language Processing (NLP), especially for the Arabic language due to scarcity of works related to it. MTR is a harder task compared to MLC, but it lends itself as a suitable representation for Emotion Analysis (EA), which is gaining more interest due to the increasing use of social media and the wide range of applications related to it. This work introduces a new study on the use of Deep Learning (DL) techniques for emotions classification and quantification in Arabic tweets.
机译:在本文中,我们介绍了一种多标签分类(MLC)方法来确定阿拉伯语推文中表达的所有情绪和多目标回归(MTR)方法,以确定情绪的强度。 MLC涉及每个样本预测零或更多类。 它是自然语言处理(NLP)中有趣的研究主题之一,特别是由于与它相关的工程稀缺而导致阿拉伯语。 与MLC相比,MTR是一个艰难的任务,但它将其自身作为情感分析(EA)的合适表示,这是由于越来越多地利用社交媒体和与其相关的应用程序的广泛应用程序而获得更多兴趣。 这项工作介绍了对Arabic Tweets中的情绪分类和量化使用深度学习(DL)技术的新研究。

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