Real Time Emotion Detection System

Author(s)	Mr. Naman Sirohi
Country	India
Abstract	Emotion detection has become a pivotal technology in human-computer interaction, mental health monitoring, and customer feedback analysis. This paper presents a comprehensive real-time emotion detection system that leverages audio, video, and text inputs using JavaScript. The system integrates multiple machine learning models to classify emotions from speech (audio), facial expressions (video), and written content (text). Built on a modular architecture, the system employs TensorFlow.js for in-browser model inference, ensuring low latency and privacy by processing data locally. Experimental results demonstrate an average accuracy of 85% across modalities, with seamless integration into web applications. The system’s scalability, cross-platform compatibility, and real-time performance make it suitable for diverse applications, from telehealth to interactive marketing.
Keywords	Emotion Detection, Multimodal Analysis, JavaScript, Real-Time Processing.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-16
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.44113
Short DOI	https://doi.org/g9kfh5

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International Journal For Multidisciplinary Research