Recent Papers

Emotion Detection using Machine Learning (ML) is an evolving discipline within Artificial Intelligence that focuses on analyzing and interpreting human emotions through computational techniques. By leveraging facial expressions, voice patterns, text-based sentiment, and physiological signals, ML models are able to automatically classify emotional states such as happiness, anger, sadness, fear, disgust, and surprise. Advancements in deep learning, especially in Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer architectures, have significantly improved the accuracy and robustness of emotion-recognition systems across real-time environments.This paper presents a comprehensive analysis of machine-learning-based emotion detection, including its fundamental principles, data sources, model architectures, applications, and recent research developments. It highlights the growing use of multimodal systems, challenges such as dataset imbalance and ethical concerns, and the integration of Explainable AI (XAI) techniques to improve transparency. The study concludes by identifying major opportunities for future research in personalized emotion modeling, real-time processing, and privacy-preserving ML frameworks. Keywords- Emotion Detection, Machine Learning, Affective Computing, Facial Expression Recognition, Speech Emotion Recognition, Natural Language Processing, Deep Learning, CNN, LSTM, Transformer Networks, Multimodal Emotion Analysis

Copyright © 2025 Akshat Gautam, Ritesh Chandel. This is an open access article distributed under the Creative Commons Attribution License.