Characterizing the Geometric and Temporal Organization of Emotion in Neural Representation Spaces

Emotion is encoded in the internal representations of language models, yet the principles governing its organization remain poorly understood. This raises a fundamental question: do emotional concepts form a structured geometry in representation space, and how does that structure evolve as emotional states unfold through conversation? We address these questions by modeling categorical, continuous, and temporal aspects of emotion through three representation forms, State, Continuous, and Dynamic, using BERT, DistilBERT, MiniLM, and RoBERTa. This choice of encoder-only Transformers provides contextualized representations while enabling comparison of emotional organization across architectures. Applying these representations to emotion classification and conversational datasets, we find that emotional concepts are broadly accessible through linear decoding. Yet this accessibility does not imply an unstructured representation. Emotional concepts concentrate in compact, structured subspaces with lower intrinsic dimensionality than the ambient representation space. The resulting organization varies with the representation objective, exposing different computational properties while preserving core geometric patterns across architectures. This organization also extends beyond individual emotional states into their evolution through dialogue. Across conversational trajectories, emotional representations exhibit reproducible temporal organization, with neighboring states showing predictive persistence and systematic changes in representation dynamics around emotional transitions. Together, these results establish a unified framework for characterizing emotion, linking the geometry of emotional states to representation learning and their evolution over time within conversational context.