Artificial Intelligence Driven Audience Sentiment Analytics for Interactive Digital Broadcasting Platforms

Richard Andre Sunarjo; Tessa  Handra; Rifqa Nabila Muti; Kamal Arif  Al-Farouqi

Authors

Richard Andre Sunarjo Universitas Pelita Harapan, Indonesia Author https://orcid.org/0009-0007-7349-2375
Tessa Handra Multimedia Nusantara University, Indonesia Author https://orcid.org/0009-0004-5375-708X
Rifqa Nabila Muti CAI Sejahtera Indonesia, Indonesia Author https://orcid.org/0009-0008-2980-3823
Kamal Arif Al-Farouqi Eduaward Incorporation, United Kingdom Author https://orcid.org/0009-0007-4074-3545

Keywords:

Audience Engagement, Sentiment Analysis, Artificial Intelligence, Digital Broadcasting Platforms, Natural Language Processing

Abstract

The rapid growth of interactive digital broadcasting platforms has significantly transformed the way audiences engage with media content through live chats, comments, and social media interactions. However, the massive volume of usergenerated feedback creates challenges for broadcasters in understanding audience sentiment efficiently. This study aims to analyze audience sentiment using Artificial Intelligence (AI) driven analytics to improve the understanding of audience engagement in interactive digital broadcasting platforms. The research applies a quantitative approach using AI based Natural Language Processing (NLP) techniques to process and analyze audience feedback data collected from comments, live chat interactions, and social media responses related to digital broadcast content. The analytical process includes data preprocessing, sentiment classification, and machine learning based modeling to identify patterns of audience emotional responses and engagement. The findings indicate that AI driven sentiment analytics can effectively classify audience opinions and detect real time sentiment trends associated with broadcasted content. The results also demonstrate that AI-based analysis enables broadcasters to gain deeper insights into audience preferences, evaluate content performance, and optimize broadcasting strategies more efficiently compared with conventional manual analysis methods. In conclusion, the integration of AI in audience sentiment analytics offers a valuable approach for enhancing audience understanding and supporting data-driven decision-making in modern digital broadcasting ecosystems while promoting more responsive and personalized media experiences.

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