Using Artificial Intelligence and Semantic Analysis to Pick-out and Manipulate Harmful Real-time Criticism

The rise of online interactions has increased the need for automated toxic comment detection. The proposed work develops a machine learning model to classify comments as toxic or non-toxic, improving digital safety and content moderation. The preprocessing stage includes text normalization, tokenization, and stop word removal to refine data for training. To handle class imbalance, various resampling techniques are applied, ensuring fair model predictions. Several supervised Machine Learning (ML) algorithms, including logistic regression, SVM (Support Vector Machine), CNN (Convolutional Neural Network) are explored, with evaluation based on precision, recall, F1-score, and accuracy for classification purposes. Advanced text vectorization methods like TF-IDF (Term Frequency – Inverse Document Frequency), word embedding are used. Model interpretability is emphasized through techniques like SHAP and LIME, providing insights into decision-making. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are interpretable AI (Artificial Intelligence) tools for Machine Learning applications. By leveraging deep learning and NLP (Natural Language Processing) advancements, this paper aims to offer an effective, scalable tool for moderating online content, helping administrators to reduce toxicity and foster healthier discussions. Additionally, real-time processing capabilities are considered to ensure timely detection of harmful content. The integration of user feedback mechanisms and adaptive learning methods further enhance the model’s efficiency, making it more responsive to evolving language patterns and contextual nuances. Future improvements may include multilingual support, sentiment analysis integration, and deployment strategies for large-scale online platforms, ensuring robust and adaptable moderation.