基于Seq2Seq方言规范化和转换的阿拉伯语面向方面的情感分类

Mohammed ElAmine Chennafi, Hanane Bedlaoui, Abdelghani Dahou, M. A. Al-qaness
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引用次数: 9

摘要

情感分析是自然语言处理中最重要的领域之一,因为它具有广泛的应用范围和使用它所带来的好处。它被定义为识别自然语言文本的情感极性。由于阿拉伯世界的社交媒体和电子商务网站上有大量用户生成的内容,研究人员最近将注意力集中在阿拉伯语SA上。该领域的研究大多集中在句子和文献层面。本研究解决了阿拉伯语的方面级情感分析,这是SA的一个较少研究的版本。由于阿拉伯语NLP具有挑战性,而且可用的阿拉伯语资源很少,阿拉伯语方言也很多,因此很少有研究试图对阿拉伯语文本进行基于方面的情感分析。具体而言,本研究考虑了两个ABSA任务:方面术语极性和方面类别极性,在完成分类任务后使用阿拉伯语方言的文本规范化。我们提出了一个用于方言规范化的Seq2Seq模型,该模型可以通过减少OOV词的数量作为ABSA分类任务的预处理步骤。因此,模型的准确性提高了。实验结果表明,我们的模型在任务和数据集上都优于文献中的现有模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Arabic Aspect-Based Sentiment Classification Using Seq2Seq Dialect Normalization and Transformers
Sentiment analysis is one of the most important fields of natural language processing due to its wide range of applications and the benefits associated with using it. It is defined as identifying the sentiment polarity of natural language text. Researchers have recently focused their attention on Arabic SA due to the massive amounts of user-generated content on social media and e-commerce websites in the Arabic world. Most of the research in this fieldwork is on the sentence and document levels. This study tackles the aspect-level sentiment analysis for the Arabic language, which is a less studied version of SA. Because Arabic NLP is challenging and there are few available Arabic resources and many Arabic dialects, limited studies have attempted to detect aspect-based sentiment analyses on Arabic texts. Specifically, this study considers two ABSA tasks: aspect term polarity and aspect category polarity, using the text normalization of the Arabic dialect after making the classification task. We present a Seq2Seq model for dialect normalization that can serve as a pre-processing step for the ABSA classification task by reducing the number of OOV words. Thus, the model’s accuracy increased. The results of the conducted experiments show that our models outperformed the existing models in the literature on both tasks and datasets.
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