AN NLP-BASED MACHINE LEARNING FRAMEWORK FOR CYBERBULLINGDETECTION THROUGH SENTIMENT ANALYSIS

Abstract

Cyberbullying has become a major problem in the digital world, with negative consequences for both individuals and the general well-being of society. Accurately identifying cyberbullying on social media platforms—which account for a sizable portion of digital communication—is a workable answer to this pervasive problem. While machine learning algorithms and pre-trained language models have been the mainstay of traditional techniques, these frequently encounter issues including excessive computational complexity and poor adaptation to subtle linguistic patterns. In order to enhance cyberbullying detection in online communication, this study suggests a sophisticated framework that combines Long Short-Term Memory (LSTM) networks with Natural Language Processing (NLP) techniques. To guarantee high-quality and noisefree input data, the system uses sophisticated text preparation techniques as tokenisation, stop word removal, stemming, and lemmatisation. Embedding techniques are used to extract contextual patterns and sentiment features while maintaining semantic information. An LSTM model, which successfully captures the sequential and temporal dependencies in textual data, is then fed these processed inputs. This model is ideal for comprehending the dynamic nature of cyberbullying language. Additionally, resampling approaches are used to improve the robustness of the model without introducing bias in order to solve class imbalance in the multi-class context. The suggested solution shows how integrating deep learning with thorough NLP improves the precision and contextual awareness needed for successful cyberbullying detection. KEYWORDS: Natural Language Processing (NLP), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs),