  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.
