Classifying Toxic Comments with Machine Learning and Deep Learning Approaches

Stepped-up development of online communication has made it essential to identify and remove toxic content to maintain an online environment free from danger. The detection and evaluation of dangerous content found in textual data requires this process. This research explores various ML and DL models for toxic comment classification, and shows comparison of them, which efficiently detects the harmful content such as threats, hate speech, cyberbullying, and offensive language. The study compares various Natural Language Processing (NLP) techniques, procedures starting with tokenization followed by word embeddings then extends to deep learning algorithms such as LSTMs, CNNs, NB and SVM for better classification results. The research also integrates a rule-based keyword detection approach for comparative evaluation. A real-time harmful information identification system works across social media and chat applications as well as meetings because it uses NLP techniques alongside machine learning models. This research analyzed preprocessed harmful comment labels using feature extraction methods on a collection of dataset through experiments that led to model results to support safer digital communication and filtering toxic comments.