An Analysis of Cyberbullying in Text Data using Deep Learning Algorithms

Cyberbullying is a peculiarity that unfavorably affects individuals; its casualties experience a scope of emotional wellness issues, including low confidence, uneasiness, forlornness, and sorrow. Cyberbullying is become more normal while online entertainment use is turning out to be more inescapable. Regular procedures to battle cyberbullying include the utilization of rules and guidelines, human mediators, and boycotts that rely upon hostile language. These strategies, however, are not scalable and perform poorly in social media. To automatically identify cyberbullying behaviours, a principled learning system must be created. Nonetheless, the process is difficult because of the brief, chaotic, and disorganised content material, as well as the deliberate obscuring of offensive phrases or words by those who use social media. We suggest using sentiment data to identify cyberbullying behaviours in social media by putting forth a sentiment-informed cyberbullying detection framework. Our approach has been inspired by sociological and psychological research on bullying behaviours and their relationship to emotions. Experiments conducted on two publicly accessible real-world social media datasets demonstrate the advantages of the suggested approach. Additional research confirms that using sentiment data to detect cyberbullying is beneficial.