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Emotion analysis of Arabic articles and its impact on identifying the author's gender

机译：阿拉伯文文章的情感分析及其对鉴定作者性别的影响

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The Gender Identification (GI) problem is concerned with determining the gender of the author of a given text based on its contents. The GI problem is one of the authorship profiling problems which have a wide range of applications in various fields such as marketing and security. Due to its importance, extensive research efforts have been invested in the GI problem for different languages. Unfortunately, the same cannot be said about the Arabic language despite its strategic importance and widespread. In this work, we explore the GI problem for Arabic text as a supervised learning problem. Specifically, we consider and compare two approaches for feature extraction. The first one is the Bag-Of-Words (BOW) approach while the second one is based on computing features related to sentiments and emotions. One goal of this work is to confirm the validity of the common stereotype that female authors tend to write in a more emotional way than male authors. Our results show that there is no conclusive evidence that this is true for our dataset.
机译：性别识别（GI）问题涉及根据文本内容确定给定文本作者的性别。地理标志问题是作者身份分析问题之一，在市场营销和安全等各个领域都有广泛的应用。由于其重要性，已经针对不同语言的GI问题进行了广泛的研究。不幸的是，尽管阿拉伯语具有战略重要性和广泛意义，但不能说相同的话。在这项工作中，我们探讨了阿拉伯文字的地理标志问题，将其作为有监督的学习问题。具体来说，我们考虑并比较了两种特征提取方法。第一种是单词袋（BOW）方法，而第二种是基于与情绪和情感有关的计算功能。这项工作的一个目标是确认女性作家比男性作家倾向于以更感性的方式书写的常见刻板印象的有效性。我们的结果表明，没有确凿的证据证明这对我们的数据集是正确的。

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