  Emotion regulation is a crucial element in dealing with emotional events and
has positive effects on mental health. This paper aims to provide a more
comprehensive understanding of emotional events by introducing a new French
corpus of emotional narratives collected using a questionnaire for emotion
regulation. We follow the theoretical framework of the Component Process Model
which considers emotions as dynamic processes composed of four interrelated
components (behavior, feeling, thinking and territory). Each narrative is
related to a discrete emotion and is structured based on all emotion components
by the writers. We study the interaction of components and their impact on
emotion classification with machine learning methods and pre-trained language
models. Our results show that each component improves prediction performance,
and that the best results are achieved by jointly considering all components.
Our results also show the effectiveness of pre-trained language models in
predicting discrete emotion from certain components, which reveal differences
in how emotion components are expressed.
