Text classification is a prominent research area, gaining more interest in academia, industry and social media. Arabic is one of the world’s most famous languages and it had a significant role in science, mathematics and philosophy in Europe in the middle ages. During the Arab Spring, social media, that is, Facebook, Twitter and Instagram, played an essential role in establishing, running, and spreading these movements. Arabic Sentiment Analysis (ASA) and Arabic Text Classification (ATC) for these social media tools are hot topics, aiming to obtain valuable Arabic text insights. Although some surveys are available on this topic, the studies and research on Arabic Tweets need to be classified on the basis of machine learning algorithms. Machine learning algorithms and lexicon-based classifications are considered essential tools for text processing. In this paper, a comparison of previous surveys is presented, elaborating the need for a comprehensive study on Arabic Tweets. Research studies are classified according to machine learning algorithms, supervised learning, unsupervised learning, hybrid, and lexicon-based classifications, and their advantages/disadvantages are discussed comprehensively. We pose different challenges and future research directions.

中文翻译：

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阿拉伯文推文分类：回顾

文本分类是一个突出的研究领域，引起了学术界，行业和社交媒体的更多兴趣。阿拉伯语是世界上最著名的语言之一，中世纪时它在欧洲的科学，数学和哲学中起着重要作用。在“阿拉伯之春”期间，社交媒体（即Facebook，Twitter和Instagram）在建立，运行和传播这些运动中起着至关重要的作用。这些社交媒体工具的阿拉伯语情感分析（ASA）和阿拉伯语文本分类（ATC）是热门话题，旨在获得宝贵的阿拉伯语文本见解。尽管可以对此主题进行一些调查，但是有关阿拉伯语推文的研究和研究仍需要基于机器学习算法进行分类。机器学习算法和基于词典的分类被认为是文本处理的重要工具。本文对以前的调查进行了比较，阐明了对阿拉伯语推文进行全面研究的必要性。研究根据机器学习算法，监督学习，无监督学习，混合和基于词典的分类进行分类，并全面讨论其优缺点。我们提出了不同的挑战和未来的研究方向。