Cyberbullying using Web Based Support Vector Machine using Recurrent Method
Abstract: 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 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.
Keywords: Cyberbullying, Text Classification, Natural Language Processing, NLP Algorithms, Naive Bayes, Support Vector Machines, Recurrent Neural Networks, Social Media, Online Safety
How to Cite?: Dr. S. Brindha, "Cyberbullying using Web Based Support Vector Machine using Recurrent Method", Volume 13 Issue 8, August 2024, International Journal of Science and Research (IJSR), Pages: 221-223, https://www.ijsr.net/getabstract.php?paperid=ES24801135720, DOI: https://dx.doi.org/10.21275/ES24801135720