Deep Learning Approaches for Effective Hate Speech Classification

Hate Speech Classification, is an essential aspect of online content management, it involves identifying and classifying objectionable language in order to promote a safer online environment. This research's main goal is to use deep learning techniques to examine and improve the classification of hate speech. In current paper we are introducing a Deep Learning Hybrid LSTM-GRU model. The Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are combined in this model. We can see improved performance of the suggested Hybrid LSTM-GRU model. The model achieved 99.90% accuracy rate, along with metrics of 99.87%, 99.76%, and 99.88% for precision, recall, and F1 score, respectively. We did a comparative analysis against other approaches like LSTM RNN, TCN+CNN, CNN MLP, and 1D CNN models, and got better results. The proposed model shows a 7% improvement over LSTM RNN as well as it shows improvements over other existing models. The model's well-balanced approach effectively addresses the nuanced challenges associated with hate speech detection in diverse online contexts. The Hybrid LSTM-GRU model showcased improved performance over traditional models and gave better results in classifying online hate speech.