An Appraisal Theoretic Approach to Modelling Affect Flow in Conversation Corpora

Alok Debnath, Yvette Graham, Owen Conlan

Keywords: emotion analysis, appraisal theory, conversational agents, affective computing

TL;DR: While emotion is important to study in conversation, there are too many schemas and annotation methods to make sense of what an emotion label means; so we present that using appraisal theory is one possible generalizable emotion annotation.

Abstract: This paper presents a model of affect in conversations by leveraging Appraisal Theory as a generalizable framework. We propose that the multidimensional cognitive model of Appraisal Theory offers significant advantages for analyzing emotions in conversational contexts, addressing the current challenges of inconsistent annotation methodologies across corpora. To demonstrate this, we present AppraisePLM, a regression and classification model trained on the crowd-EnVent corpus that outperforms existing models in predicting 21 appraisal dimensions including \textit{pleasantness}, \textit{self-control}, and \textit{alignment with social norms}. We apply AppraisePLM to diverse conversation datasets spanning task-oriented dialogues, general-domain chit-chat, affect-specific conversations, and domain-specific affect analysis. Our analysis reveals that AppraisePLM successfully extrapolates emotion labels across datasets, while capturing domain-specific patterns in affect flow -- change in conversational emotion over the conversation. This work highlights the entangled nature of affective phenomena in conversation and positions affect flow as a promising model for holistic emotion analysis, offering a standardized approach to evaluate and benchmark affective capabilities in conversational agents.

Submission Number: 129