A Machine Learning Approach to Hate Speech and Toxicity Classification.

The rapid expansion of online platforms has led to the proliferation of harmful content, including hate speech and toxic language. These types of content not only disrupt healthy discussions but can also create hostile environments for users. This project proposes a machine learning-based approach to classify hate speech and toxic content in text, using the Roberta transformer model. The system is trained on the "Synthetic_data" dataset, which contains labeled examples representing various toxicity levels. The Roberta algorithm, known for its ability to understand contextual language, is leveraged to predict toxicity levels in text as normal, medium, or high. The proposed system aims to automate content moderation by efficiently categorizing and filtering toxic language, providing a scalable and reliable solution for online platforms. A user-friendly interface allows users to submit text for classification, while administrators can monitor system performance and manage content. The system will be evaluated using performance metrics such as accuracy, precision, recall, and F1 score to ensure its reliability. The ultimate goal of the project is to enhance online interaction quality by mitigating the impact of toxic language.

Keywords: Hate Speech, Toxicity Classification, Machine Learning, Roberta, Text Classification, Toxic Content, Online Platforms, Content Moderation, Transformer Models, Natural Language Processing.