Sentiment and Toxicity: Hate Speech Detection with Machine Learning and NLP

Abstract

This report presents a machine learning and natural language processing (NLP)-based system for detecting and moderating hate speech and toxicity on online platforms. With the exponential growth of user-generated content, managing and mitigating toxic behavior has become a critical challenge to ensure a safer online environment. The proposed solution leverages advanced NLP techniques and supervised machine

learning models trained on a large, annotated dataset. By employing transformer- based architectures like BERT, the system is designed to identify hate speech and

toxic language in real time across various social media and communication platforms. The methodology incorporates data preprocessing, feature extraction, and model optimization to achieve high accuracy while addressing challenges such as linguistic ambiguity and context dependency. Experimental results demonstrate the effectiveness of the system in detecting toxicity with a competitive accuracy rate, outperforming traditional methods. This work not only highlights the technical feasibility of automated hate speech detection but also underscores its societal impact by fostering healthier online interactions. The findings contribute to the ongoing efforts to combat digital toxicity and can serve as a foundation for further advancements in this field.