Combining Semantic Analysis and Deep Neural Networks for Identifying Hate Speech

As internet and social media proliferate human lives, hateful content also becomes a critical challenge to be mitigated to safeguard users and organizations. With millions of posts and comments generated every day, relying solely on manual detection methods is impractical. Moreover, hate speech often evolves in subtle ways, making it difficult to track using predefined rules or simple keyword-based methods. This necessitates the development of automated systems capable of understanding and adapting to the nuanced and context-dependent nature of hate speech.The proposed approach uses the Deep Neural Network for identifying potential hate speech based on the conjugate back propagation model, along with term and group frequencies as additional features. It has been shown that the proposed approach attains higher classification accuracy compared to existing work in the domain