AI-BASED HATE SPEECH DETECTION USING NLTK AND MACHINE LEARNING

Abstract

“AI-Based Hate Speech Detection Using NLTK and Machine Learning” presents an intelligent text classification system designed to automatically identify and filter hate speech content from online platforms. The increasing use of social media has led to the rapid spread of abusive, offensive, and harmful content, making manual moderation inefficient and impractical. The proposed system utilizes Natural Language Toolkit (NLTK) for text preprocessing, including tokenization, stop-word removal, stemming, and feature extraction, to convert raw text into structured data. Machine learning algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression are then applied to classify text into categories such as hate speech, offensive language, and neutral content. The system is trained on labeled datasets to improve detection accuracy and reduce false positives. Experimental results show that combining NLTK-based preprocessing with machine learning models significantly enhances classification performance. The proposed approach helps in promoting safer online environments by enabling automated content moderation and reducing the spread of harmful speech on digital platforms.