检测仇恨:基于语音推文的卷积神经网络和机器学习算法
Hameda A Sennary1, Ghada Abozaid2, Ashraf Hemeida3
1Department of Mathematics, Faculty of Science, Aswan University, Aswân, Egypt.
Scientific reports
|November 21, 2024
概括
本研究引入了一个术语频率-反向文档频率 (TF-IDF) 方法,用于机器学习模型在社交媒体上自动检测仇恨言论,达到99%以上的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算社会科学 计算社会科学
背景情况:
- 社交媒体促进信息共享,但也使仇恨言论等有害内容成为可能.
- 开发自动化系统来识别和减轻仇恨言论对于在线安全至关重要.
研究的目的:
- 开发和评估基于机器学习的方法,用于在社交媒体平台上自动识别仇恨言论.
- 评估术语频率-反向文档频率 (TF-IDF) 在仇恨言论检测中的特征工程的有效性.
主要方法:
- 利用TF-IDF对来自三个不同的数据集的文本数据进行特征工程:"Davidson等人发表的仇恨言论攻击性推文. ","Twitter仇恨言论",以及一个合并的数据集,包括"网络欺凌数据集 (毒性_解析_数据集)".
- 评估了11个机器学习和深度学习分类器:逻辑回归 (LR),天真贝叶斯 (NB),多层感知器 (MLP),支持矢量机器 (SVM),随机森林 (RF),K-最近邻居 (KNN),K-Means,决策树 (DT),梯度提升分类器 (GBC),额外树 (ET) 和卷积神经网络 (CNN).
主要成果:
- 基于TF-IDF的特征工程方法,当应用于11个分类器时,在识别仇恨言论方面表现出高效.
- 在评估的模型中实现了超过99%的最大准确性,这表明在仇恨言论检测方面表现强.
结论:
- 提出的基于TF-IDF的功能工程方法对于使用机器学习和深度学习模型自动检测仇恨言论非常有效.
- 该研究强调了计算方法在线仇恨言论挑战的潜力,为内容调节提供了可扩展的解决方案.
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