AI Powered Anti-Cyber Bullying System using Machine Learning Algorithm of Multinomial Naive Bayes and Optimized Linear Support Vector Machine

TL;DR

This paper presents an AI-powered anti-cyber bullying system that uses machine learning algorithms, specifically Multinomial Naive Bayes and an optimized linear SVM, achieving 92% accuracy in detecting and intercepting bullying messages.

Contribution

The study introduces a novel AI system combining MNB and optimized SVM for reliable cyber bullying detection and interception, including a chatbot for testing.

Findings

Achieved 92% accuracy in detecting bullying messages

Developed an automated system for intercepting harmful messages

Implemented a chatbot to validate the model's effectiveness

Abstract

"Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue." ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naive Bayes (MNB) and optimized linear Support Vector Machine (SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action.