Detection and Classification of Cyberbullying in Social Media using Text Mining

This research work intends to classify the texts associated with bullying contents in social media, especially twitter by using the text mining process. A Multi-Modal Detection and classification of Cyberbullying media is developed in the study. This model integrates textual, and metadata to identify the cyberbullying media in case of social networks. The process involves two phases training and test the cyberbullying data, where natural language processing (NLP) is applied as the pre-processing tool and then particle swarm optimisation is used as feature selection process. Finally, the study applies decision tree classifier to classify the instances associated with cyberbullying and after classification, these features are combined with text instances to detect the performance of the proposed model. The simulation is conducted to test the detection rate of the classifier than the existing methods. The results show that the proposed method achieves higher rate of classification and detection accuracy than the existing methods.