Mental Health Disturbance has many reasons and cyberbullying is one of the major causes that does exploitation using social media as an instrument. The cyberbullying is done on the basis of Religion, Ethnicity, Age and Gender which is a sensitive psychological issue. This can be addressed using Natural Language Processing with Deep Learning, since social media is the medium and it generates massive form of data in textual form. Such data can be leveraged to find the semantics and derive what type of cyberbullying is done and who are the people involved for early measures. Since deriving semantics is essential we proposed a Hybrid Deep Learning Model named 1-Dimensional CNN-Bidirectional-LSTMs with Residuals shortly known as Res-CNN-BiLSTM. In this paper we have proposed the architecture and compared its performance with different approaches of Embedding Deep Learning Algorithms.

翻译：心理健康混乱有许多原因,网络欺凌是利用社交媒体作为工具进行剥削的主要原因之一。网络欺凌是基于宗教、种族、年龄和性别的,这是一个敏感的心理问题。这可以通过与深层学习有关的自然语言处理来解决,因为社交媒体是媒体,以文字形式生成大量数据。这些数据可以用来查找语义,并得出网络欺凌的种类以及哪些人参与早期措施。由于生成语义至关重要,我们提出了一个名为1-Dimional CNN-Bidirectal-LSTM的混合深层学习模型,与不久被称为Res-CNN-BILSTM的残余物一起使用。我们在此文件中提出了这一结构,并用嵌入深层学习阿尔戈特姆的不同方法对它的表现进行了比较。