Intelligent Toxic Comment Detection Using Machine Learning And Natural Language Processing Techniques

The rapid expansion of social media platforms and online communication systems has significantly increased the amount of user-generated content on the internet. While these platforms enable people to share ideas and communicate freely, they also expose users to harmful content such as hate speech, offensive language, cyberbullying, and abusive comments. Toxic comments not only affect healthy online discussions but also create negative psychological and social impacts on individuals. Therefore, developing automated systems capable of detecting and filtering toxic comments has become an important research problem in natural language processing and online content moderation. This study presents an intelligent framework for detecting toxic comments using machine learning and natural language processing techniques. The proposed system analyses textual data collected from online platforms and classifies comments into toxic and non-toxic categories. Various preprocessing techniques such as tokenization, stop-word removal, text normalization, and lemmatization are applied to clean and prepare the dataset for model training. Feature extraction methods including Term Frequency–Inverse Document Frequency (TF-IDF) and word embedding techniques are used to transform textual data into numerical representations suitable for machine learning models. Several machine learning and deep learning algorithms, including Naive Bayes, Support Vector Machines (SVM), Logistic Regression, and Convolutional Neural Networks (CNN), are implemented and compared to determine the most effective model for toxic comment classification. The models are evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. Experimental results indicate that deep learning models, particularly CNN-based architectures, achieve higher classification accuracy and better performance in detecting complex toxic language patterns. The proposed system can assist online platforms in automatically identifying harmful content and maintaining safer digital communication environments. By integrating machine learning techniques with advanced natural language processing methods, the framework contributes to improving online content moderation and promoting respectful interactions in digital communities.