基于神经网络的有害评论检测与删除

A. Wadhwani, Priyank Jain, Shriya Sahu
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引用次数: 1

摘要

在这个网络世界里有很多交流的方式。随着这个日益增长的时代,在安全可靠的环境中也存在许多障碍。网络欺凌和虐待呈指数级增长。深度学习方法最近开始被用于检测在线论坛上的辱骂性评论。在线辱骂语言的检测和分类是一项非同小可的NLP挑战,因为在线评论是在各种各样的上下文中发表的,并且包含来自许多不同正式和非正式词汇的单词。为了克服这个问题,我们设计了一个模型来检测消息中的毒性水平,并用另一个短语替换它。它使用深度神经网络模型,将消息/评论作为输入,并检查各种参数,如有毒,严重有毒,身份仇恨,威胁等。应用程序最后用另一个单词/短语替换该部分。检查你关心的东西可能会很麻烦。网络滥用和挑衅的危险意味着许多人不再交流,转而寻求改变想法。阶段斗争充分鼓励讨论,推动许多网络限制或完全关闭客户评论。
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Injurious Comment Detection and Removal utilizing Neural Network
There are a lot of ways to communicate in this cyber world. With this increasingly growing era there is also much obstruction in a safe and secure environment. There has been an exponential growth in cyber bullying and abusing. Deep learning methods have recently begun to be used to detect abusive comments made in online forums. Detecting, and classifying, online abusive language is a non-trivial NLP challenge because online comments are made in a wide variety of contexts, and contain words from many different formal and informal lexicons. For this to overcome we design a model that detects the level of toxicity in a message and replaces it with another phrase. It uses a Deep Neural network model that takes a message/comment as an input and checks for various parameters such as Toxic, Severe Toxic, Identity hate, threat, etc. And the application finally then replaces the portion with another word/phrase. Examining things, you care about can be troublesome. The danger of misuse and provocation online implies that numerous individuals quit communicating and offer up on looking for changed thoughts. Stages battle to adequately encourage discussions, driving numerous networks to restrict or totally shut down client remarks.
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