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ABSTRACT
The proposed system leverages advanced machine learning and natural language processing techniques to detect cyberbullying with high accuracy and scalability. It addresses the limitations of manual moderation, which is time-consuming and prone to errors. Traditional keyword-based filtering fails to capture the nuances of language, often missing subtle forms of cyberbullying. By employing deep learning models and contextual word embeddings, the system can understand the semantic meaning of text, enhancing detection accuracy. Real-time monitoring and intervention capabilities allow for immediate action against cyberbullying incidents, preventing escalation and mitigating harm. The system's web-based API facilitates seamless integration into various platforms, promoting widespread accessibility. Additionally, the automated approach reduces operational costs compared to manual moderation, allowing resources to be allocated more effectively. The system's objective and consistent detection methods also reduce human bias, fostering a fair and inclusive online environment. Overall, this innovative solution offers a comprehensive approach to creating safer digital spaces by combining cutting-edge technology with user-centric design
