  The term emotion analysis in text subsumes various natural language
processing tasks which have in common the goal to enable computers to
understand emotions. Most popular is emotion classification in which one or
multiple emotions are assigned to a predefined textual unit. While such setting
is appropriate for identifying the reader's or author's emotion, emotion role
labeling adds the perspective of mentioned entities and extracts text spans
that correspond to the emotion cause. The underlying emotion theories agree on
one important point; that an emotion is caused by some internal or external
event and comprises several subcomponents, including the subjective feeling and
a cognitive evaluation. We therefore argue that emotions and events are related
in two ways. (1) Emotions are events; and this perspective is the fundament in
natural language processing for emotion role labeling. (2) Emotions are caused
by events; a perspective that is made explicit with research how to incorporate
psychological appraisal theories in NLP models to interpret events. These two
research directions, role labeling and (event-focused) emotion classification,
have by and large been tackled separately. In this paper, we contextualize both
perspectives and discuss open research questions.
