一种分类和量化阿拉伯语推文多重情绪的深度学习方法

Faisal Abdullah, M. Al-Ayyoub, Ismail Hmeidi, Nouh Alhindaw
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引用次数: 2

摘要

在本文中,我们引入了一种多标签分类(MLC)方法来确定阿拉伯语推文中表达的所有情绪,以及一种多目标回归(MTR)方法来确定情绪的强度。MLC涉及对每个样本的零个或多个类别的预测。它是自然语言处理(NLP)中一个有趣的研究课题,特别是阿拉伯语,由于缺乏与之相关的作品。与MLC相比,MTR是一项更困难的任务,但它可以作为情感分析(EA)的合适表示,由于社交媒体的使用越来越多,以及与之相关的广泛应用,情感分析正获得越来越多的兴趣。这项工作介绍了一项关于在阿拉伯语推文中使用深度学习(DL)技术进行情绪分类和量化的新研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Deep Learning Approach to Classify and Quantify the Multiple Emotions of Arabic Tweets
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.
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