Anti social comment classification based on kNN algorithm

Nidhi Chandra, S. Khatri, S. Som
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引用次数: 11

Abstract

Billions of contributions are made every day across multiple online communities and social media websites in the form of social messages, social blogs and online discussion. The aim of this paper is to identify such comments and posts which are racist and malicious in nature so that they could be effetely banned and removed in order to counter them. This article uses set of documents with racist comments as text corpus on which appropriate machine learning algorithm is applied to detect racist comments or meaning. To detect anti-social content there is a need to find the extent of similarity between a pair of text messages as a source and classified terms which are antisocial or in discriminating terms. The approach devised in this article to detect antisocial behavior is a technique based on term frequency based content classification.
基于kNN算法的反社会评论分类
每天在多个在线社区和社交媒体网站上,以社交信息、社交博客和在线讨论的形式做出数十亿的贡献。本文的目的是确定这些评论和帖子是种族主义和恶意的性质,以便他们可以有效地禁止和删除,以对抗他们。本文使用一组带有种族主义评论的文档作为文本语料库,并在其上应用适当的机器学习算法来检测种族主义评论或含义。为了检测反社会内容,需要找到作为来源的一对短信与反社会或歧视性术语的分类术语之间的相似性程度。本文设计的检测反社会行为的方法是一种基于词频的内容分类技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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