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Emotion stimulus detection is the task of finding the cause of an emotion ina textual description, similar to target or aspect detection for sentimentanalysis. Previous work approached this in three ways, namely (1) as textclassification into an inventory of predefined possible stimuli ("Is thestimulus category A or B?"), (2) as sequence labeling of tokens ("Which tokensdescribe the stimulus?"), and (3) as clause classification ("Does this clausecontain the emotion stimulus?"). So far, setting (3) has been evaluated broadlyon Mandarin and (2) on English, but no comparison has been performed.Therefore, we aim to answer whether clause classification or sequence labelingis better suited for emotion stimulus detection in English. To accomplish that,we propose an integrated framework which enables us to evaluate the twodifferent approaches comparably, implement models inspired by state-of-the-artapproaches in Mandarin, and test them on four English data sets from differentdomains. Our results show that sequence labeling is superior on three out offour datasets, in both clause-based and sequence-based evaluation. The onlycase in which clause classification performs better is one data set with a highdensity of clause annotations. Our error analysis further confirmsquantitatively and qualitatively that clauses are not the appropriate stimulusunit in English.

Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection