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Hate Speech Detection Through Innovative Hybrid Deep Learning Techniques

Year: 2025

Type: article

Abstract: 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.

Source: International Research Journal of Modernization in Engineering Technology and Science

DOI: 10.56726/irjmets77433

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4413433408

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