Cyberbullying, in spite of being a common phenomenon, has been posing various obstacles while employing natural language processing (NLP). To leverage NLP techniques in the detection of cyberbullying, the corpus must be annotated with accurate sentiments and targeted. This will improve the accuracy at which the sentiment targets are being predicted. In this study, we have incorporated the BERT model. We have fine-tuned the model using the hate speech Twitter dataset. Named entity recognition (NER) has been explored for its efficacy in identifying aspect-targets from tweets that don’t contain the “@” symbol in the username explicitly. Our proposed system shows better results than the existing state-of-the-art tools used for sentiment analysis.