Detection of Cyberbullying in Social Media

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We have presented comprehensive reviews on Natural Language (NLP) based approach to enhance the sentiment classi�cation by adding semantics in feature vectors and thereby using ensemble methods for classi�cation. Adding semantically similar words and context-sense identities to the feature vectors will increase the accuracy of prediction. In addition, SHA-1 authentication mechanism will use to strengthen the authentication.