面向社交媒体的Afan Oromo仇恨言论文本检测机器学习方法的超参数调优

Naol Bakala Defersha, Kula Kekeba, K. Kaliyaperumal
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引用次数: 3

摘要

随着社交媒体网络在语言多样化和多元文化的发展中国家(如埃塞俄比亚)的迅速渗透,在线用户的对话变得越来越随意和多语言。仇恨言论文本系统的紧急情况。为此,尽管在线用户在不同的社交媒体平台上使用许多其他语言,但针对英语和法语等资源丰富的语言开发了各种自动仇恨言论检测和分类系统。阿凡奥罗莫语是社交媒体用户用来表达感受、情绪和分享信息的一种自然语言。因此,迫切需要开发一种智能系统,可以自动检测和分类仇恨言论,特别是对于资源稀缺的埃塞俄比亚本土语言,如Afan Oromo。这项工作是关于从评论和帖子中识别仇恨言论文本,这些评论和帖子是用资源可怕的贫穷语言Afan Oromo生成的。我们准备了第一个包含社交媒体评论和帖子的Afan Oromo仇恨言论文本检测数据集。然后采用n-gram和TF-IDF两种特征选择方法进行特征选择。在选取重要特征后将自然语言处理任务应用于数据集上。我们从默认参数和调优参数中应用了六个机器学习分类器来检测仇恨言论文本帖子和评论。实验表明,支持向量机比分类器在Oromo仇恨语音文本检测数据集上的f值高出92%。这个Afan Oromo仇恨言论文本数据集公开可在https://www.naolinfo.info/for进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tuning Hyperparameters of Machine Learning Methods for Afan Oromo Hate Speech Text Detection for Social Media
With the rapidly growing penetration of social media networks in linguistically diverse and multicultural developing nations like Ethiopia, the conversations of online users have increasingly become more casual and multilingual. The emergency of hate speech text system. To this end, various automated hate speech detection and classification systems have been developed for resource-rich languages such as English and French even though online users are using many other languages on different social media platforms. Afan Oromo is one natural language used by social media users to express feelings, emotions and share messages. Hence, there is an urgent need for the development of an intelligent system that can automatically detect and classify hate speech, especially for resource-scarce indigenous Ethiopian languages like Afan Oromo. This work is about the identification of hate speech text from comments and posts generated in resource scary poor language Afan Oromo. We prepared first hate speech text detection dataset of Afan Oromo that containing comments and posts from social media. Then, n-gram and TF-IDF feature selection approaches were employed to select features. After the important feature selected Natural language processing tasks applied on the dataset. We applied six machine learning classifiers from default and tuned parameters to detect hate speech text posts and comments. The experiment show that Support Vector Machine outperform 92% values of F-measure than classifiers Afan Oromo hate speech text detection dataset. This Afan Oromo hate speech text dataset publicly available on https://www.naolinfo.info/for further research.
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