EXPLAINABLE MULTIMODAL AI FOR SENTIMENT AND INTEGRITY ANALYSIS IN DIGITAL JOURNALISM
DOI:
https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6956Keywords:
Artificial Intelligence, Emotional Expression Recognition, Narrative Accuracy Assessment, Multimodal Learning, Sentiment and Prosody Analysis, Explainable Media AnalyticsAbstract [English]
The study attempts to deal with the increasing problem of the objective assessment of sentimental expression and narrative truth in broadcast media where editorial biases, sentimental manipulation, and narrative distortion may play a significant role in the species perception. Manual analysis is an aspect of traditional content analysis that has strong dependence on subjective judgment and is not scalable or consistent. The overall goal of the study is to establish an AI-based analytical tool that can objectively evaluate sentimental processes and confirm the logic of the narrative in the practice of the broadcast media. The offered methodology uses a multimodal AI framework with the combination of computer vision, speech processing, and natural language understanding. Transformer based attention mechanisms are used in combination with facial expression recognition, vocal prosody analysis and textual sentiment modeling to identify temporal sentimental patterns. The accuracy in narratives is assessed by use of semantic consistency analysis, event sequence modeling and alignment of facts across sources, which allow the detection of sentimental bias, exaggeration and informational drift. It is evaluated experimentally on curated data of broadcast news and documentary data annotated with sentiment categories and that the narrative is true-to-life. The results show that the suggested framework achieves a higher accuracy in sentiment classification and narrative inconsistency detection with more than 15 percent improvement over traditional sentiment and rule-based analysis methods. The system also offers understandable results that point out sentimentally dominated parts and plot digressions and enhances transparency and editorial responsibility. In general, this paper shows that AI-aided, explainable, and scalable analysis is an efficient tool to improve the quality control and ethical reporting and confidence of the audience in the sentimentally motivated and information-sensitive broadcast media setting.
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Copyright (c) 2025 Dr. Shweta Bajaj, Kiran Ingale, Dinesh Kumar Nayak, Rohit Kunar, Dr. Akbar Ahmad, Dr. Anil Bhanudas Pawar
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