August 16, 2025International Journal for Research in Applied Science and Engineering Technology

Human Sentiment Recognition

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The framework achieves 92% classification precision in facial emotion recognition, enabling real-time analysis.
It employs deep learning architectures and computer vision methods to identify seven emotional states within video streams.
Using TensorFlow/Keras and OpenCV, it processes video at 30 frames per second while detecting facial regions.
This development highlights significant potential for adaptive interfaces and behavioral analytics in various applications.

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Implication

This framework demonstrates 92% classification precision in facial emotion recognition, suggesting capabilities for adaptive interfaces and psychological assessments.

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Bharath K.Y (2025) studied this question.

synapsesocial.com/papers/68af59ddad7bf08b1eadea0b https://doi.org/10.22214/ijraset.2025.73652

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Implication

This framework demonstrates 92% classification precision in facial emotion recognition, suggesting capabilities for adaptive interfaces and psychological assessments.

Human Sentiment Recognition

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Implication

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Human Sentiment Recognition

Key Points

Discussion

Implication

Cite This Study

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