Analyzing and Predicting Emotional Responses in Cyber Bullying Cases: A Deep Learning Approach

Nisher Ahmed; Md Emran Hossain; Zakir Hossain; Md Farhad Kabir; Iffat Sania Hossain

doi:10.55927/fjmr.v4i4.166

Authors

Nisher Ahmed Westcliff University
Md Emran Hossain Westcliff University
Zakir Hossain California State University, Northridge
Md Farhad Kabir University of Southern California
Iffat Sania Hossain California State University

DOI:

https://doi.org/10.55927/fjmr.v4i4.166

Keywords:

Cyber Bullying Cases, A Deep Learning Approach, Emotional Responses

Abstract

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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