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Aggression Detection on Social Media Text Using Deep Neural Networks

机译:使用深神经网络对社交媒体文本的侵略性检测

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In the past few years, bully and aggressive posts on social media have grown significantly, causing serious consequences for victims/users of all demographics. Majority of the work in this field has been done for English only. In this paper, we introduce a deep learning based classification system for Face-book posts and comments of Hindi-English Code-Mixed text to detect the aggressive behaviour of/towards users. Our work focuses on text from users majorly in the Indian Subcontinent. The dataset that we used for our models is provided by TRAC-1 in their shared task. Our classification model assigns each Facebook post/comment to one of the three predefined categories: "Overtly Aggressive", "Covertly Aggressive" and "Non-Aggressive". We experimented with 6 classification models and our CNN model on a 10 K-fold cross-validation gave the best result with the prediction accuracy of 73.2%.
机译:在过去的几年里,社交媒体上的欺负和积极的帖子显着增加,对所有人口统计数据的受害者/用户来说造成严重后果。这一领域的大部分工作仅用于英语。在本文中,我们介绍了一个基于深入的学习的分类系统,用于面对面书的帖子和印度英语代码混合文的评论,以检测/向用户的激进行为。我们的工作侧重于主要在印度次大陆的用户的文本。我们用于我们模型的数据集由TRAC-1在其共享任务中提供。我们的分类模式将每个Facebook发布/评论分配给三个预定义类别之一:“明显激进的”,“隐蔽的侵略性”和“非侵略性”。我们尝试了6种分类模型,10 k折叠交叉验证的CNN模型得到了最佳结果,其预测精度为73.2%。

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