AI-ASSISTED ANALYSIS OF EMOTIONAL EXPRESSION AND NARRATIVE ACCURACY IN BROADCAST MEDIA PRACTICES

Sakshi Singh; Jay Vasani; Rakesh Kumar; Harshada Bhushan Magar; Monali Gulhane; Aishwarya Sunil Chavan

doi:10.29121/shodhkosh.v6.i5s.2025.6955

Authors

Sakshi Singh Assistant Professor, School of Fine Arts & Design, Noida International University, Noida, Uttar Pradesh, India
Jay Vasani Department of Computer Science and Engineering, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
Dr. Rakesh Kumar Assistant Professor, School of Journalism and Mass Communication, AAFT University of Media and Arts, Raipur, Chhattisgarh-492001, India
Dr. Harshada Bhushan Magar Assistant Professor, Department of Electronics and Telecommunication, AISSMS Institute of Information Technology, Kennedy Road, Pune-411001, India
Monali Gulhane Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
Aishwarya Sunil Chavan Assistant Professor, Department of Chemical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra-411037, India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6955

Keywords:

Artificial Intelligence, Emotional Expression Analysis, Narrative Accuracy, Broadcast Media Analytics, Multimodal Learning, Explainable AI

Abstract [English]

This paper proposes an AI-enhanced model of the analysis of affective expression and storytelling validity in the modern broadcasting media practice. The content broadcast is more and more shaping the perception of the masses, but the systematic analysis of the emotional coloring and the factual integrity is mostly subjective and time-consuming. The suggested solution combines the multimodal artificial intelligence models used to analyze visual cues, vocal prosody, linguistic structure, and contextual metadata simultaneously across news, documentary and televised stories. The architectures used are deep convolutional and transformer-based to identify facial micro-expressions, gesture dynamics, speech intensity, sentiment polarity and discourse level narrative flow. The attention-based mechanisms incorporate these features so as to model temporal affective paths as well as to estimate the consistency of expressed emotion, narrative purpose, and confirmed information sources. Narrative accuracy is tested through a combination of semantic consistency tests, cross-source fact-checking, and event-sequence tests, which allow finding out cases of exaggeration, emotional discrimination, or narrative drift. Emotional classification and increased accuracy in detecting narrative inconsistencies in annotated broadcast datasets have been shown to be more precise than traditional content analysis and more reliable than conventional methods in validity. The structure also offers interpretable graphical explanations and verbal explanations that favour transparency to the editors, reporters and regulators. The proposed AI-assisted methodology can make broadcast narratives more responsible, increase the confidence of the audience, and provide useful tools to control quality in broadcasting situations with assertive emotionality and susceptible to information, thereby making broadcasting environments safer. It is possible that in the future, this can be expanded to cross-cultural emotion models and live broadcast governance and ethical frameworks monitoring.

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