Intelligent Content Moderation for Real-Time Chat Platforms Using Natural Language Processing

This research presents the design and implementation of a real-time chat platform integrated with an intelligent automated moderation system using Natural Language Processing (NLP) techniques. The proposed framework combines full-stack web technologies with machine learning-based text classification to analyze user-generated messages before they are distributed across the communication platform. The system utilizes WebSocket-based real-time communication and a moderation pipeline capable of detecting harmful, abusive, or toxic language. By incorporating automated content analysis directly into the messaging workflow, the platform aims to create a safer and more responsible digital communication environment while maintaining low-latency interaction. Experimental evaluation demonstrates that the moderation mechanism effectively identifies inappropriate content with an observed detection effectiveness of approximately 90–94% while preserving efficient message transmission performance. The study highlights the potential of integrating NLP-driven moderation systems into modern communication platforms to improve online safety and user experience.