Emotion Space

Emotion space research aims to model and manipulate emotions computationally, focusing on representing emotional states in a continuous, multi-dimensional space, often using valence and arousal as key axes. Current research utilizes various machine learning approaches, including generative adversarial networks (GANs), transformer-based models, and hierarchical regression chains, to analyze and synthesize emotional expressions in text, speech, and facial expressions. This work has significant implications for improving human-computer interaction, enabling more nuanced and empathetic AI systems, and advancing our understanding of human emotion itself through computational analysis of large literary and speech corpora.