Recent studies have shown that cyberbullying is a rising youth epidemic. In this paper, we develop a novel automated classification model that identifies the cyberbullying texts without fitting them into large dimensional space. On the other hand, a classifier .cannot provide a limited convergent solution due to its overfitting problem. Considering such limitations, we developed a text classification engine that initially pre-processes the tweets, eliminates noise and other background information, extracts the selected features and classifies without data overfitting. The study develops a novel Deep Decision Tree classifier that utilizes the hidden layers of Deep Neural Network (DNN) as its tree node to process the input elements. The validation confirms the accuracy of classification using the novel Deep classifier with its improved text classification accuracy.

最近的研究表明，网络欺凌是一种正在上升的青年流行病。在本文中，我们开发了一种新颖的自动分类模型，该模型可以识别网络欺凌文本，而无需将其放入大尺寸空间。另一方面，由于分类器过拟合的问题，它无法提供有限的收敛解。考虑到这些限制，我们开发了一种文本分类引擎，该引擎首先对推文进行预处理，消除噪声和其他背景信息，提取所选功能并进行分类而不会出现数据过度拟合的情况。该研究开发了一种新颖的深度决策树分类器，该分类器利用深度神经网络（DNN）的隐藏层作为其树节点来处理输入元素。