ItemConference Paper

An Efficent Deep Learning Framework for Cyberbullying Detection Using DistilBERT and Sentiment Analysis

Title
An Efficent Deep Learning Framework for Cyberbullying Detection Using DistilBERT and Sentiment Analysis
Creator
Kumar, Ravinder; Sharma, Dimpal; Kumar, Ajay; Hemrajani, Naveen; Poonia, Ramesh Chandra
Description
Particularly because of the complex and changing character of online communication, which hampers conventional detection strategies, the frequency of cyberbullying presents a significant threat to mental health and well-being in the digital age. This article presents a fast deep learning approach to improve cyberbullying detection by combining sentiment analysis with a lightweight transformer model, DistilBERT. This work intends to increase classification performance by using sentiment-based features and using DistilBERT's language and contextual awareness. Unlike conventional approaches and simpler machine learning methods, which can depend on feature extraction techniques like Bag of Words (BOW) or TF-IDF, the proposed model directly leverages contextual embeddings. Moreover, DistilBERT provides a balance between speed and performance unlike deep learning models like CNN, BLSTM, and LSTM, which could suffer with computational efficiency. Experimental results demonstrating remarkable accuracy and recall on many different datasets indicate the effectiveness of our hybrid approach. demonstrating a significant rise in cyberbullying detection over conventional methods, to evaluate performance criteria including computational efficiency, accuracy, and F1-score. With an outstanding 93.7 % accuracy, the proposed model exceeded earlier evaluated methods on this dataset. 2025 IEEE.
Source
2025 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2025 - Proceedings;pp.185-190
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Deep Learning; Detection Cyberbullying; DistilBERT; Natural Language Processing; Sentiment Analysis
Coverage
Kumar R., Jecrc University, Department of Computer Science & Engineering, Jaipur, India; Sharma D., Jecrc University, Department of Computer Science & Engineering, Jaipur, India; Kumar A., Jecrc University, Department of Computer Science & Engineering, Jaipur, India; Hemrajani N., Jecrc University, Department of Computer Science & Engineering, Jaipur, India; Poonia R.C., Department of Computer Science, Christ University, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152565-1;
Format
online
Language
English
Type
Conference paper

Citation

Kumar, Ravinder; Sharma, Dimpal; Kumar, Ajay; Hemrajani, Naveen; Poonia, Ramesh Chandra, “An Efficent Deep Learning Framework for Cyberbullying Detection Using DistilBERT and Sentiment Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25839.

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