Cyberbullying Detection using Natural Language Processing

Abstract

Abstract: Around the world, the use of the Internet and social media has increased exponentially, and they have become an integral part of daily life. It allows people to share their thoughts, feelings, and ideas with their loved ones through the Internet and social media. But with social networking sites becoming more popular, cyberbullying is on the rise. Using technology as a medium to bully someone is known as Cyberbullying. The Internet can be a source of abusive and harmful content and cause harm to others. Social networking sites provide a great medium for harassment, bullies, and youngsters who use these sites are vulnerable to attacks. Bullying can have long-term effects on adolescents’ ability to socialize and build lasting friendships Victims of cyberbullying often feel humiliated. social media users often can hide their identity, which helps misuse the available features. The use of offensive language has become one of the most popular issues on social networking. Text containing any form of abusive conduct that displays acts intended to hurt others is offensive language. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and sometimes forces them to commit suicide. The purpose of this project is to develop a technique that is effective to detect and avoid cyberbullying on social networking sites we are using Natural Language Processing and other machine learning algorithms. The dataset that we used for this project was collected from Kaggle, it contains data from Twitter that is then labeled to train the algorithm. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed Model for cyberbullying dataset shows that Logistic Regression performs better and achieves good accuracy than SVM, Ransom forest, Naive-Bayes, and Xgboost algorithm. Keywords: Cyberbullying, Machine learning, Natural language processing, Social media, Kaggle, Dataset.