Emotion Action Detection and Emotion Inference: the Task and Dataset

TL;DR

This paper introduces a new dataset and two tasks for emotion analysis in NLP, focusing on cause and action events to better understand emotional contexts and inference.

Contribution

It presents the Cause-Emotion-Action Corpus with annotations for emotion, cause, and action, and defines two novel tasks: emotion causality and emotion inference.

Findings

Baseline results show significant room for improvement.

The dataset contains 10,603 samples and 15,892 events.

Initial analysis demonstrates the complexity of emotion causality and inference.

Abstract

Many Natural Language Processing works on emotion analysis only focus on simple emotion classification without exploring the potentials of putting emotion into "event context", and ignore the analysis of emotion-related events. One main reason is the lack of this kind of corpus. Here we present Cause-Emotion-Action Corpus, which manually annotates not only emotion, but also cause events and action events. We propose two new tasks based on the data-set: emotion causality and emotion inference. The first task is to extract a triple (cause, emotion, action). The second task is to infer the probable emotion. We are currently releasing the data-set with 10,603 samples and 15,892 events, basic statistic analysis and baseline on both emotion causality and emotion inference tasks. Baseline performance demonstrates that there is much room for both tasks to be improved.