  Emotion classification in NLP assigns emotions to texts, such as sentences or
paragraphs. With texts like "I felt guilty when he cried", focusing on the
sentence level disregards the standpoint of each participant in the situation:
the writer ("I") and the other entity ("he") could in fact have different
affective states. The emotions of different entities have been considered only
partially in emotion semantic role labeling, a task that relates semantic roles
to emotion cue words. Proposing a related task, we narrow the focus on the
experiencers of events, and assign an emotion (if any holds) to each of them.
To this end, we represent each emotion both categorically and with appraisal
variables, as a psychological access to explaining why a person develops a
particular emotion. On an event description corpus, our experiencer-aware
models of emotions and appraisals outperform the experiencer-agnostic
baselines, showing that disregarding event participants is an
oversimplification for the emotion detection task.
