Real Time Audience Analytics Using Machine Learning to Measure Listener and Viewer Cultural Engagement

Asep Sutarman; Felix Sutisna; Dimas Aditya Prabowo; Kgomotso Moyo

Authors

Asep Sutarman University of Muhammadiyah Prof. Dr. HAMKA, Indonesia Author https://orcid.org/0009-0002-1029-7963
Felix Sutisna Universitas Multimedia Nusantara, Management, Indonesia Author https://orcid.org/0009-0005-0503-6736
Dimas Aditya Prabowo Pandawan Group, Indonesia Author https://orcid.org/0009-0004-4448-2053
Kgomotso Moyo Mfinitee Incorporation, South Africa Author https://orcid.org/0009-0005-5779-562X

Keywords:

Real-Time Analytics, Machine Learning, Cultural Engagement, Natural Language Processing, Audience Sentiment Analysis

Abstract

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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References

[1] Q. Aini, D. Manongga, U. Rahardja, I. Sembiring, and Y.-M. Li, “Understanding behavioral intention to use of air quality monitoring solutions with emphasis on technology readiness,” International Journal of Human–Computer Interaction, pp. 1–21, 2024.

[2] R. Ahli, M. F. Hilmi, and A. Abudaqa, “Moderating effect of perceived organizational support on the relationship between employee performance and its determinants: A case of entrepreneurial firms in uae,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 2, pp. 199–212, 2024.

[3] U. B. AN Syamsudin, “Sentiment analysis of user suggestions for mandatory e-learning using text mining on the learning management system,” Technomedia Journal, vol. 9, no. 3, pp. 346–359, 2025.

[4] A. Ruangkanjanases, A. Khan, O. Sivarak, U. Rahardja, and S.-C. Chen, “Modeling the consumers’ flow experience in e-commerce: The integration of ecm and tam with the antecedents of flow experience,” SAGE Open, vol. 14, no. 2, p. 21582440241258595, 2024.

[5] T. R. V Jericho, “Implementation of cnn and mediapipe in increasing the effectiveness of stretching in futsal sports,” Technomedia Journal, vol. 9, no. 3, pp. 386–397, 2025.

[6] U. Rahardja, Q. Aini, A. S. Bist, S. Maulana, and S. Millah, “Examining the interplay of technology readiness and behavioural intentions in health detection safe entry station,” JDM (Jurnal Dinamika Manajemen), vol. 15, no. 1, pp. 125–143, 2024.

[7] R. Sivaraman, M.-H. Lin, M. I. C. Vargas, S. I. S. Al-Hawary, U. Rahardja, F. A. H. Al-Khafaji, E. V. Golubtsova, and L. Li, “Multi-objective hybrid system development: To increase the performance of diesel/photovoltaic/wind/battery system.” Mathematical Modelling of Engineering Problems, vol. 11, no. 3, 2024.

[8] T. Hidayat, D. Manongga, Y. Nataliani, S. Wijono, S. Y. Prasetyo, E. Maria, U. Raharja, I. Sembiring et al., “Performance prediction using cross validation (gridsearchcv) for stunting prevalence,” in 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). IEEE, 2024, pp. 1–6.

[9] N. L. D. R. A. S. M Rakhmansyah, RA Sunarjo, “Evaluating the success of project management information system implementation to enhance work team effectiveness,” Technomedia Journal, vol. 9, no. 3, pp. 398–407, 2025.

[10] D. Sundar, “Architectural advancements for ai/ml-driven tv audience analytics and intelligent viewership characterization,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 3, no. 1, pp. 124–132, 2022.

[11] S. H. S. S. N. L. F Delia, HR Ngemba, “Application of k-means clustering algorithm in population density

grouping,” Technomedia Journal, vol. 9, no. 3, pp. 373–385, 2025.

[12] A. Vrochidis, C. Tsita, N. Dimitriou, S. Krinidis, S. Panagiotidis, S. Parcharidis, D. Tzovaras, and

V. Chatzis, “User perception and evaluation of a deep learning framework for audience engagement analysis in mass events,” in International Conference on Human-Computer Interaction. Springer, 2023, pp. 268–287.

[13] N. S. M. K. M. D. B. PA Sunarya, U Rahardja, “Impact of waterfall method on systematic academic information system development,” Technomedia Journal, vol. 9, no. 3, pp. 360–372, 2025.

[14] A. Abudaqa, R. Alzahmi, M. F. Hilmi, and G. Ahmed, “How supply chain resilience is achieved in smes of dubai, uae? considering the flexible supply chain practices as a mediator,” International Journal of Logistics Systems and Management, vol. 45, no. 2, pp. 159–174, 2023.

[15] L. Honesti, Q. Aini, M. I. Setiawan, N. P. L. Santoso, and W. Y. Prihastiwi, “Smart contract-based gamification scheme for college in higher education,” APTISI Transactions on Management, vol. 6, no. 2, pp. 102–111, 2022.

[16] A. Abudaqa, R. A. Alzahmi, M. F. Hilmi, and H. AlMujaini, “Impact of job security, career advancement and employee participation on employee engagement in oil and gas industry, uae,” Universidad y Sociedad, vol. 15, no. 3, pp. 26–36, 2023.

[17] U. Rahardja, Q. Aini, A. Khairunisa, P. A. Sunarya, and S. Millah, “Implementation of blockchain technology in learning management system (lms),” APTISI Transactions on Management, vol. 6, no. 2, pp. 112–120, 2022.

[18] T. Alblooshi, M. Azli, M. F. Hilmi, A. Abudaqa, and G. Ahmed, “Examining the trends in citizen satisfaction towards e-government services in united arab emirates: a structural equation modelling approach,” International Journal of Services, Economics and Management, vol. 14, no. 1, pp. 58–77, 2023.

[19] A. Abudaqa, R. Alzahmi, M. F. Hilmi, and G. Ahmed, “How organisational leadership and strategic management helps in business excellence: moderating role of employees’ motivation in uae,” International Journal of Business Excellence, vol. 33, no. 2, pp. 191–209, 2024.

[20] E. N. Pratama, E. Suwarni, and M. A. Handayani, “The effect of job satisfaction and organizational commitment on turnover intention with person organization fit as moderator variable,” Aptisi Transactions on Management, vol. 6, no. 1, pp. 74–82, 2022.

[21] L. M. Gutta, T. R. Bammidi, R. K. Batchu, and N. Kanchepu, “Real-time revelations: advanced data analysis techniques,” International Journal of Sustainable Development Through AI, ML and IoT, vol. 3, no. 1, pp. 1–22, 2024.

[22] E. Pebriyanti and O. Kusmayadi, “Brand ambassador and brand personality on decision to purchase nature republic in karawang,” APTISI Transactions on Management, vol. 6, no. 1, pp. 83–90, 2022.

[23] K. El Fayq, S. Tkatek, and L. Idouglid, “Enhancing tv program success prediction using machine learning by integrating people meter audience metrics with digital engagement metrics,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 39, no. 1, pp. 353–363, 2025.

[24] U. Rahardja, I. J. Dewanto, A. Djajadi, A. P. Candra, and M. Hardini, “Analysis of covid 19 data in indonesia using supervised emerging patterns,” APTISI Transactions on Management, vol. 6, no. 1, pp. 91–101, 2022.

[25] H. Hamdan, H. Cahyadi, K. Vaher, and A. Ratih, “Ai-driven optimization of pulsed dc sputtering for enhanced indium tin oxide films,” International Transactions on Artificial Intelligence, vol. 4, no. 1, pp. 85–94, 2025.

[26] C. E. Benson, C. H. Okolo, and O. Oke, “Enhancing audience engagement through predictive analytics: Ai models for improving content interactions and retention,” Shodhshauryam, International Scientific Refereed Research Journal, vol. 6, no. 4, pp. 121–134, 2023.

[27] A. Jaya, H. Zainarthur, A. Sijabat, A. R. Dina, and A. Faturahman, “Assessing user satisfaction in hadirku through an extended tam framework,” International Transactions on Artificial Intelligence, vol. 4, no. 1, pp. 73–84, 2025.

[28] L. H. Aziz, B. Callula, A. Rozi, and M. Madani, “Life cycle assessment of silicon photovoltaics and their environmental impacts,” International Transactions on Artificial Intelligence, vol. 4, no. 1, pp. 61–72, 2025.

[29] R. Evans, F. P. Oganda, M. A. Setiawan, L. Nurjanah, and M. Sunengsih, “Assessing the environmental and economic effects of smart grid integration using sem,” International Transactions on Artificial Intelligence, vol. 4, no. 1, pp. 49–60, 2025.

[30] A. G. Privitera, F. Fontana, and M. Geronazzo, “The role of audio in immersive storytelling: a systematic review in cultural heritage,” Multimedia Tools and Applications, vol. 84, no. 16, pp. 16 105–16 143, 2025.

[31] Q. Aini, H. Setiyowati, R. S. Ikhsan, A. Amroni, and L. Pasha, “Integrating midjourney scripts into architectural design for aesthetic innovation,” International Transactions on Artificial Intelligence, vol. 4, no. 1, pp. 37–48, 2025.

[32] C. Li, X. Weng, Y. Li, and T. Zhang, “Multimodal learning engagement assessment system: An innovative approach to optimizing learning engagement,” International Journal of Human–Computer Interaction, vol. 41, no. 5, pp. 3474–3490, 2025.

[33] R. G. Munthe, M. Abbas, R. Fernandez, and N. Ulita, “The impact of educational information systems on learning accessibility in higher education,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 94–103, 2024.

[34] C. Surana-S ˜ anchez and M. E. Aramendia-Muneta, “Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda,” International Journal of Consumer Studies, vol. 48, no. 2, p. e13027, 2024.

[35] R. Aprianto, E. P. Lestari, E. Fletcher et al., “Harnessing artificial intelligence in higher education: Balancing innovation and ethical challenges,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 84–93, 2024.

[36] L. Ryan Bengtsson and J. Edlom, “Commodifying participation through choreographed engagement: the taylor swift case,” Arts and the Market, vol. 13, no. 2, pp. 65–79, 2023.

[37] N. Anwar, J. Anderson, T. Williams et al., “Applying data science to analyze and improve student learning outcomes in educational environments,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 72–83, 2024.

[38] C. H. Lee, N. Gobir, A. Gurn, and E. Soep, “In the black mirror: Youth investigations into artificial intelligence,” ACM Transactions on Computing Education, vol. 22, no. 3, pp. 1–25, 2022.

[39] L. Meria, C. S. Bangun, and J. Edwards, “Exploring sustainable strategies for education through the adoption of digital circular economy principles,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 62–71, 2024.

[40] D. Xi, J. Zhou, W. Xu, and L. Tang, “Discrete emotion synchronicity and video engagement on social media: A moment-to-moment analysis,” International Journal of Electronic Commerce, vol. 28, no. 1, pp. 108–144, 2024.

[41] R. A. Sunarjo, M. H. R. Chakim, S. Maulana, and G. Fitriani, “Management of educational institutions through information systems for enhanced efficiency and decision-making,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 47–61, 2024.

[42] A. Palomba, “Advancing predictive content analysis: a natural language processing and machine learning approach to television script data,” Journal of Marketing Analytics, vol. 13, no. 3, pp. 824–845, 2025.

[43] A. Levordashka, D. Stanton Fraser, and I. D. Gilchrist, “Measuring real-time cognitive engagement in remote audiences,” Scientific Reports, vol. 13, no. 1, p. 10516, 2023.

[44] A.-M. Gioti, A. Einbond, and G. Born, “Composing the assemblage: Probing aesthetic and technical dimensions of artistic creation with machine learning,” Computer Music Journal, vol. 46, no. 4, pp. 62– 80, 2022.

[45] E. B. Kang, “Ground truth tracings (gtt): On the epistemic limits of machine learning,” Big Data & Society, vol. 10, no. 1, p. 20539517221146122, 2023.

[46] G. C. Dobre, M. Gillies, and X. Pan, “Immersive machine learning for social attitude detection in virtual reality narrative games,” Virtual Reality, vol. 26, no. 4, pp. 1519–1538, 2022.

[47] X. Chen and Z. Ibrahim, “A comprehensive study of emotional responses in ai-enhanced interactive installation art,” Sustainability, vol. 15, no. 22, p. 15830, 2023.

[48] S. T. Boppiniti, “Exploring the synergy of ai, ml, and data analytics in enhancing customer experience and personalization,” International Machine learning journal and Computer Engineering, vol. 5, no. 5, pp. 1–26, 2022.

[49] C. Pabba and P. Kumar, “An intelligent system for monitoring students’ engagement in large classroom teaching through facial expression recognition,” Expert Systems, vol. 39, no. 1, p. e12839, 2022.

[50] K. I. Hossain, “Literature-based language learning: Challenges, and opportunities for english learners,” Ampersand, vol. 13, p. 100201, 2024.