An Empowering Safer Online Spaces: Advancements in Comprehensive Cyberbullying Detection through NLP and Machine Learning Algorithms

The pervasive nature of on line communique has caused an increase in cyberbullying, necessitating sturdy detection mechanisms.This study gives a comprehensive technique to on line cyberbullying detection utilizing Natural Language Processing (NLP) and machine studying algorithms.By leveraging a diverse dataset encompassing diverse social media platforms, we pre-method the records to handle linguistic nuances, consisting of slang and emojis.The method includes characteristic extraction the use of strategies like TF-IDF and phrase embeddings, and version training with algorithms together with Support Vector Machines (SVM), Random Forest, and deep getting to know fashions like LSTM and BERT.We compare the fashions based totally on precision, bear in mind, F1-rating, and accuracy, demonstrating that BERT achieves superior performance due to its contextual expertise abilities.Additionally, the observe addresses the undertaking of imbalanced datasets via oversampling and SMOTE techniques, ensuring sturdy model training.The proposed gadget now not simplest detects explicit kinds of cyberbullying but additionally subtle and oblique bureaucracy, improving its applicability in actual-world scenarios.This research contributes to the field by presenting a scalable and powerful answer for on-line cyberbullying detection, aiming to foster more secure on line environments and support the improvement of proactive measures towards cyberbullying.