Authors: Mr. Appalla Yazna Surya Sai Kiran, Miss. Savarapu Suhasini
Abstract: The rapid growth of social media platforms and online communication has significantly increased the volume of user-generated content, creating new challenges in identifying toxic language, hate speech, cyberbullying, and abusive comments. These harmful interactions negatively affect online communities, user well-being, and digital safety, highlighting the need for intelligent and automated content moderation systems. This paper presents ToxiShield, a next-generation intelligent framework for toxic comment detection that integrates Machine Learning (ML) and Natural Language Processing (NLP) techniques to accurately classify online comments as toxic or non-toxic. The proposed framework employs comprehensive text preprocessing, including tokenization, stop-word removal, text normalization, lemmatization, and feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF) and word embedding techniques to generate meaningful textual representations. To evaluate the effectiveness of the proposed framework, multiple classification algorithms, including Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, and Convolutional Neural Networks (CNN), are implemented and comparatively analysed using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that deep learning-based models, particularly CNN, achieve superior performance in identifying complex contextual toxicity patterns compared with traditional machine learning methods. The proposed ToxiShield framework provides an efficient, scalable, and intelligent solution for automated online content moderation, contributing to safer digital communication environments and promoting respectful interactions across social media platforms and online communities.
