Sentiment Analysis For Cyberbullying Detection Using NLP And LSTM

Abstract

 The phenomenon of cyberbullying has emerged as a critical challenge in the digital landscape, posing detrimental effects on individuals and broader societal well-being. A practical solution to this widespread issue involves the accurate identification of cyberbullying within social media platforms, which constitute a significant share of digital communication. While traditional approaches have primarily utilized machine learning algorithms and pre-trained language models, these often face challenges such as high computational complexity and limited adaptability to nuanced linguistic patterns. This paper proposes an advanced framework that leverages Natural Language Processing (NLP) techniques combined with Long Short-Term Memory (LSTM) networks to improve cyberbullying detection in online text. The framework applies refined text preprocessing steps—such as tokenization, stop word removal, stemming, and lemmatization—to ensure high-quality and noise-free input data. Sentiment features and contextual patterns are extracted using embedding methods to preserve semantic information. These processed inputs are then fed into an LSTM model, which effectively captures the sequential and temporal dependencies in textual data, making it well-suited for understanding the dynamic nature of cyberbullying language. Additionally, to address class imbalance in the multi-class setting, resampling techniques are employed, improving the model's robustness without inducing bias. The proposed system demonstrates that combining deep learning with comprehensive NLP enhances the accuracy and contextual understanding required for effective cyberbullying detection.