Toxic comments classification

With the widespread growth of digital communication, the presence of harmful content, including hate speech and abusive language, has become a significant concern. This study introduces an approach that utilizes Machine Learning (ML) and Natural Language Processing (NLP) techniques to identify and classify toxic comments efficiently. The system integrates various ML and deep learning models, including Logistic Regression, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, to improve classification accuracy. The research elaborates on the methodologies employed, examines experimental findings, and discusses potential improvements to enhance online content moderation.