Abstract

This paper presents a novel approach for cyberbullying detection and classification in social media text using an ensemble model that combines BERT (Bidirectional Encoder Representations from Transformers) and Support Vector Machine (SVM) with grid search for multiclass classification. We have also compared the performance of our proposed with various machine and deep learning models and the results show that our proposed model outperforms other models achieving an accuracy of 90% on testing data. Further, we have used to used SHapley Additive exPlanations (SHAP) an Explainable (XAI) technique to interpret the predictions of the BERT-SVM ensemble model.