Cyberbullying using Web Based Support Vector Machine using Recurrent Method

Cyberbullying is a growing concern in the digital age, necessitating effective detection and prevention measures. This research focuses on addressing cyberbullying through text classification using Natural Language Processing (NLP) algorithm -based techniques. The study involves the compilation of a diverse dataset containing social media posts, messages, and comments, which are labelled as either cyberbullying or non -cyberbullying content. The text data is pre -processed to handle noise, remove stop words, and tokenize the text for NLP analysis. Experimental results demonstrate the efficacy of NLP algorithm -based techniques in cyberbullying text classification, outperforming traditional rule -based methods. The system's ability to identify harmful content aids in early detection and intervention, promoting safer online environments. Experimental results demonstrate the efficacy of NLP algorithmbased techniques in cyberbullying text classification, outperforming traditional rule -based methods. The system's ability to identify harmful content aids in early detection and intervention, promoting safer online environments.