Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches

TL;DR

This paper explores the relationship between emotion role labeling and event-focused emotion classification in NLP, emphasizing their theoretical connection and proposing integrated research directions.

Contribution

It bridges two separate approaches in emotion analysis—role labeling and event causality—by contextualizing their relationship and identifying open research questions.

Findings

Emotions are fundamentally related to events in text.

Emotion theories highlight the dual role of events as causes and components of emotions.

The paper discusses open challenges in integrating emotion role labeling with event-based approaches.

Abstract

The term emotion analysis in text subsumes various natural language processing tasks which have in common the goal to enable computers to understand emotions. Most popular is emotion classification in which one or multiple emotions are assigned to a predefined textual unit. While such setting is appropriate for identifying the reader's or author's emotion, emotion role labeling adds the perspective of mentioned entities and extracts text spans that correspond to the emotion cause. The underlying emotion theories agree on one important point; that an emotion is caused by some internal or external event and comprises several subcomponents, including the subjective feeling and a cognitive evaluation. We therefore argue that emotions and events are related in two ways. (1) Emotions are events; and this perspective is the fundament in natural language processing for emotion role labeling. (2) Emotions are caused by events; a perspective that is made explicit with research how to incorporate psychological appraisal theories in NLP models to interpret events. These two research directions, role labeling and (event-focused) emotion classification, have by and large been tackled separately. In this paper, we contextualize both perspectives and discuss open research questions.