在阿拉伯语Twitter数据上识别仇恨言论的系统文献综述:研究挑战和未来方向
Ali Alhazmi1,2, Rohana Mahmud1, Norisma Idris1
1Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
PeerJ. Computer science
|April 25, 2024
概括
这篇评论检查了阿拉伯语在推特中的仇恨言论识别,发现机器学习和N-gram/CBOW功能是最常见的. 它指导研究人员了解NLP领域的趋势和挑战.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 文本挖掘 (Text Mining) 是一个很好的方法.
- 计算语言学 计算语言学
背景情况:
- 自动识别仇恨言论对于在线安全至关重要.
- 阿拉伯语推特对NLP任务提出了独特的语言挑战.
- 对阿拉伯语仇恨言论检测的研究已经显著增长.
研究的目的:
- 系统地审查有关阿拉伯语仇恨言论自动识别的文献.
- 确定2018-2023年研究趋势,共同的方法和挑战.
- 为这个领域的未来研究提供指导.
主要方法:
- 在九个学术数据库中进行系统的文献审查.
- 根据预先定义的标准,对24项选定的研究进行了分析.
- 语言变种,仇恨言论类别,分类,特征工程,性能指标和验证方法的检查.
主要成果:
- 现代标准阿拉伯语 (MSA) 是仇恨言论中最普遍的语言品种.
- 机器学习技术主导着阿拉伯的仇恨言论识别.
- N-gram和CBOW是经常使用的特征工程技术,F1得分,精度,回忆和准确性作为常见的指标.
结论:
- 具有N-gram/CBOW功能的机器学习模型显示了阿拉伯语仇恨言论检测的前景.
- 列车/测试分割是主要的验证方法.
- 结果为提高模型效率提供了指导,并突出了政策和社区管理中的应用.
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