Experiencer-Specific Emotion and Appraisal Prediction

TL;DR

This paper introduces a novel approach to emotion classification in NLP that considers individual experiencers within texts, using both categorical and appraisal-based representations, leading to improved performance over traditional methods.

Contribution

It proposes a new task focusing on experiencer-specific emotion and appraisal prediction, advancing beyond prior models that ignore participant perspectives.

Findings

Experiencer-aware models outperform baselines in emotion prediction.

Incorporating appraisal variables enhances understanding of emotional responses.

Focusing on event participants improves emotion detection accuracy.

Abstract

Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs. With texts like "I felt guilty when he cried", focusing on the sentence level disregards the standpoint of each participant in the situation: the writer ("I") and the other entity ("he") could in fact have different affective states. The emotions of different entities have been considered only partially in emotion semantic role labeling, a task that relates semantic roles to emotion cue words. Proposing a related task, we narrow the focus on the experiencers of events, and assign an emotion (if any holds) to each of them. To this end, we represent each emotion both categorically and with appraisal variables, as a psychological access to explaining why a person develops a particular emotion. On an event description corpus, our experiencer-aware models of emotions and appraisals outperform the experiencer-agnostic baselines, showing that disregarding event participants is an oversimplification for the emotion detection task.