Emotion Classification

Emotion classification, aiming to automatically identify and categorize human emotions from various data sources like text, speech, physiological signals, and images, is a rapidly evolving field. Current research emphasizes improving accuracy and robustness across diverse modalities and languages, often employing deep learning architectures such as transformers, recurrent neural networks, and convolutional neural networks, along with techniques like multimodal fusion and contrastive learning. This work holds significant implications for various applications, including mental health monitoring, human-computer interaction, and disaster response, by enabling more nuanced and empathetic interactions between humans and technology. Furthermore, ongoing efforts focus on addressing biases in datasets and improving the interpretability of models.