<![CDATA[Cyberbullying is a threat on any digital platform, and it can have a very harmful and emotional effect on the person receiving these types of comments. Here, we present a deep learning framework that utilizes NLP-based and neural network-based approaches to analyze and predict the emotional responses associated with cyberbullying incidents. The model is trained and evaluated using a curated dataset of social media posts labeled with emotions like anger, sadness, fear, and neutrality. Tokenization, lemmatization, and word embeddings (GloVe, BERT, etc.) are the different preprocessing methods used to represent textual data. Write. Multiple architectures, such as CNNs, LSTM networks, and transformer-based approaches, are compared to achieve high accuracy in emotional response classification. Experimental results show that transformer models outperform traditional learning models for precision and recall. The results can lead to intelligent monitoring systems that identify harmful emotional content followed by necessary, timely interventions. Such research shows promise for AI-driven emotion analysis to support safer online environments.]]>
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