Abstract:

With the increasing prevalence of online abuse and harmful content, hate speech detection has become an essential task in maintaining safe digital environments. This paper presents a deep learning-based approach to automatically identify and classify hate speech in online text. Leveraging advanced models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformer-based architectures, we analyze large and diverse datasets of hate speech. Various preprocessing methods—including tokenization, lemmatization, and vectorization—are employed to enhance the accuracy of these models. Furthermore, sentiment analysis and feature extraction are integrated to boost classification performance. Experimental results show that our deep learning models significantly outperform traditional techniques in terms of precision, recall, and F1-score. Overall, the study demonstrates that the combination of deep learning and sophisticated text analysis techniques provides a powerful solution for detecting and reducing hate speech across social media platforms.

Description:

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