HATE SPEECH DETECTION THROUGH INNOVATIVE HYBRID DEEP LEARNING TECHNIQUES

This paper presents a Python-based hate speech detection system using a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks (CLSTM).The model is compared against a traditional Support Vector Classifier (SVC) to evaluate performance.Natural Language Processing (NLP) techniques such as tokenization, lemmatization, and TF-IDF are used for text preprocessing and feature extraction.The system is modular, scalable, and designed for real-time detection, enabling effective identification of subtle hate speech across platforms.