Identifying and Classifying Toxic Comments in Cloud Using Amazon Web Services Comprehend Technology

Abstract

Growing concerns about toxic remarks on digital platforms need effective detection and categorization systems to make the internet safer. Amazon Web Services (AWS) Comprehend automates dangerous material discovery and categorization using cloud-based Natural Language Processing (NLP). The goal is to increase automatic content moderation and detection accuracy using AWS Comprehends deep learning. The goal is to use sentiment analysis and entity identification to categorize harmful comments into specified categories for real-time content filtering and reduce human moderation. To identify toxic language, minimize false positives, and enable proactive action, a scalable and adaptable solution is needed. AWS Comprehend classifies abusive or improper language by analyzing user-generated content context and sentiment using powerful NLP models. Cloud-based deployment supports high-volume text analysis with low latency and easy interaction with varied digital ecosystems. Using AWS's scalable infrastructure, poison detection may be continually optimized to respond to changing language trends and damaging talk. This strategy reduces toxic remarks, enforces content management, and promotes ethical communication on online platforms while adhering to platform standards, improving digital safety.