Shades of grey : quantifying a database of 18 aesthetic moods in classical Chinese poetry

Abstract

Understanding aesthetic moods in classical Chinese poetry is essential for decoding Chinese aesthetics and can significantly benefit culturally and aesthetically inspired creative fields. However, research on how these aesthetic moods are perceived and deconstructed is limited. To address this gap, this study quantitatively identified 18 distinctive aesthetic mood clusters in classical Chinese poetry by empirical methods including natural language processing (NLP). These clusters were paired with relevant tools: mood-eliciting images, the circular valence-arousal model, and diary episodes associated with specific poems. The outcomes were developed into a website that serves as a practical database, visualizing the granularity of aesthetic moods expressed in classical Chinese poetry and relevant elements for mood-focused research and practice.