论文标题
RES-CNN-BILSTM网络，用于克服由于通过社交媒体进行网络欺凌引起的心理健康障碍
Res-CNN-BiLSTM Network for overcoming Mental Health Disturbances caused due to Cyberbullying through Social Media
论文作者
论文摘要
心理健康障碍有很多原因，网络欺凌是使用社交媒体作为工具进行剥削的主要原因之一。网络欺凌是根据宗教，种族，年龄和性别进行的，这是一个敏感的心理问题。这可以使用自然语言处理和深度学习来解决，因为社交媒体是媒介，并且以文本形式产生了大量的数据。可以利用此类数据来找到语义，并得出哪种类型的网络欺凌行为，以及涉及早期措施的人。由于得出语义是必不可少的，因此我们提出了一个杂交深度学习模型，称为1维CNN-Bixirectional-LSTM，其残留物不久称为RES-CNN-BILSTM。在本文中，我们提出了架构，并将其性能与嵌入深度学习算法的不同方法进行了比较。
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.
