Emotion Action Detection and Emotion Inference: the Task and Dataset
This work addresses a gap in NLP for emotion analysis by providing a dataset and tasks that integrate emotions with cause and action events, though it is incremental as it builds on existing emotion classification efforts.
The authors tackled the lack of datasets for analyzing emotions in event contexts by introducing the Cause-Emotion-Action Corpus with 10,603 samples and 15,892 events, proposing two new tasks (emotion causality and emotion inference) and showing baseline performance that indicates significant room for improvement.
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.