  Emotion stimulus detection is the task of finding the cause of an emotion in
a textual description, similar to target or aspect detection for sentiment
analysis. Previous work approached this in three ways, namely (1) as text
classification into an inventory of predefined possible stimuli ("Is the
stimulus category A or B?"), (2) as sequence labeling of tokens ("Which tokens
describe the stimulus?"), and (3) as clause classification ("Does this clause
contain the emotion stimulus?"). So far, setting (3) has been evaluated broadly
on Mandarin and (2) on English, but no comparison has been performed.
Therefore, we aim to answer whether clause classification or sequence labeling
is better suited for emotion stimulus detection in English. To accomplish that,
we propose an integrated framework which enables us to evaluate the two
different approaches comparably, implement models inspired by state-of-the-art
approaches in Mandarin, and test them on four English data sets from different
domains. Our results show that sequence labeling is superior on three out of
four datasets, in both clause-based and sequence-based evaluation. The only
case in which clause classification performs better is one data set with a high
density of clause annotations. Our error analysis further confirms
quantitatively and qualitatively that clauses are not the appropriate stimulus
unit in English.
