<![CDATA[The rapid evolution of digital media has transformed audience interaction, yet traditional metrics like views and likes fail to capture the nuanced emotional and cultural dynamics of broadcast content. This study develops a real-time audience analytics framework using machine learning to measure deep cultural engagement and emotional resonance within digital media environments. Adopting a hybrid methodological approach, the research integrates Natural Language Processing (NLP) with qualitative interpretation. The system processes live interaction data, employing sentiment analysis and pattern recognition to categorize audience responses into complex emotional and cultural engagement tiers beyond simple polarity. Findings demonstrate that the machine learning model effectively identifies real-time shifts in audience sentiment, revealing how specific cultural cues trigger heightened engagement and collective emotional responses. This research advances audience analytics by bridging the gap between computational speed and qualitative depth, offering a scalable model for broadcasters and researchers to understand the cultural impact of digital content as it happens.]]>
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