机器学习技术在移动应用中仇恨语音检测的应用

Bujar Raufi, Ildi Xhaferri
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引用次数: 7

摘要

通过各种平台和应用程序的数据激增正在不断增加。数据的多功能性和无所不在使得很难检测到来源的可信度和意图。这在移动应用程序等动态环境中非常明显。因此,设计能够监视、控制和阻止任何类型恶意行为的移动应用程序非常重要。本文在这个方向上进行了尝试,实现了一种轻量级的机器学习分类方案,用于移动应用中阿尔巴尼亚语的仇恨言论检测。最初的测试和评估表明,在需要频繁和实时训练算法的移动环境中,分类器具有良好的准确性。
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Application of Machine Learning Techniques for Hate Speech Detection in Mobile Applications
The proliferation of data through various platforms and applications is in constant increase. The versatility of data and its omnipresence makes it very hard to detect the trustworthiness and intention of the source. This is very evident in dynamic environments such as mobile applications. As a result, designing mobile applications that will monitor, control and block any type of malintents is important. This paper makes an attempt in this direction by implementing a lightweight machine learning classification scheme for hate speech detection in Albanian Language for mobile applications. Initial testing and evaluations indicate good classifier accuracy in mobile environments where frequent and real-time training of the algorithm is required.
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