Ai-Powered Media Analytics: Transforming Audience Engagement Through Cloud-Native Solutions

Abstract

The media and entertainment industry encounters substantial obstacles in managing dispersed audience information across numerous platforms, establishing impediments to efficient content customization and advertising enhancement. Conventional broadcast systems demonstrate insufficient capacity for handling the speed and diversity of contemporary streaming information, causing restricted audience understanding and diminished engagement statistics. Cloud-based frameworks driven by artificial intelligence and big data technologies present revolutionary solutions for media organizations attempting to consolidate separate data flows into unified analytical environments. Apache Spark functions as the computational foundation for extensive data processing, facilitating real-time analytics capabilities capable of managing millions of streaming events per second while preserving sub-second response intervals. Sophisticated machine learning models, specifically Neural Collaborative Filtering systems, exhibit exceptional performance in content recommendation situations, attaining accuracy enhancements through deep learning structures capturing complex user-item relationships. Real-time sentiment evaluation technologies utilize transformer-based models for processing social media discussions and viewer responses, delivering detailed insights into audience preferences and content reception. The article proposes and validates a scalable framework integrating Neural Collaborative Filtering, Apache Spark, and BERT transformers for unified media analytics, demonstrating comprehensive solutions for cross-platform audience evaluation and real-time content optimization. Integration of real-time bidding systems with audience analytics enables advanced advertising optimization strategies, processing millions of bid requests per second while maintaining accurate targeting capabilities. Distributed computing frameworks enable global scalability through geographically distributed architectures maintaining consistent performance across multiple regions. Cross-platform analytics integration facilitates comprehensive audience evaluation transcending individual platform boundaries, providing unified insights into viewer behavior across the complete content ecosystem.