ASSESSMENT OF DIVERSE TECHNIQUES FOR CYBER BULLYING DETECTION ON TWITTER USING SENTIMENT ANALYSIS

Cyberbullying has emerged as a significant concern in today’s digital era, especially with the widespread use of social media platforms and online communication tools. This project presents an automated cyberbullying detection system that makes use of Natural Language Processing (NLP) techniques to identify and evaluate harmful or abusive comments in real time. The system architecture comprises a user-friendly frontend interface, a backend server for processing, a dedicated NLP engine for text analysis, and a database to store user information with reputation scores. Once a user sends in a comment, it is analyzed for bullying content, and appropriate actions such as warnings or blocking are triggered based on the severity of the detected behavior. Additionally, the system updates the user’s reputation score dynamically and notifies them of any actions taken. This approach ensures a safer and more respectful digital environment, lessens the strain on human moderators, and encourages responsible online communication. The project also explores various applications across educational platforms, gaming communities, workplace communication, and social media, demonstrating its broad scope and real-world relevance.