Emotion Carrier Recognition from Personal Narratives

TL;DR

This paper introduces Emotion Carrier Recognition, a new task in narrative understanding that identifies text fragments conveying emotions in personal narratives, supported by a manually annotated corpus and evaluation strategies.

Contribution

It presents the novel task of Emotion Carrier Recognition, a new annotated dataset, and evaluation methods for fine-grained emotion analysis in personal narratives.

Findings

Proposed a new task for fine-grained emotion detection.

Created a manually annotated corpus for ECR.

Explored machine learning models and evaluation metrics for ECR.

Abstract

Personal Narratives (PN) - recollections of facts, events, and thoughts from one's own experience - are often used in everyday conversations. So far, PNs have mainly been explored for tasks such as valence prediction or emotion classification (e.g. happy, sad). However, these tasks might overlook more fine-grained information that could prove to be relevant for understanding PNs. In this work, we propose a novel task for Narrative Understanding: Emotion Carrier Recognition (ECR). Emotion carriers, the text fragments that carry the emotions of the narrator (e.g. loss of a grandpa, high school reunion), provide a fine-grained description of the emotion state. We explore the task of ECR in a corpus of PNs manually annotated with emotion carriers and investigate different machine learning models for the task. We propose evaluation strategies for ECR including metrics that can be appropriate for different tasks.