Emotion Carrier Recognition from Personal Narratives
This work addresses a fine-grained understanding problem in narrative analysis for natural language processing, but it appears incremental as it builds on existing emotion classification tasks.
The paper introduces Emotion Carrier Recognition (ECR) as a novel task for understanding personal narratives, focusing on identifying text fragments that carry emotions, and explores machine learning models and evaluation metrics for this task.
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.