  Emotion arcs capture how an individual (or a population) feels over time.
They are widely used in industry and research; however, there is little work on
evaluating the automatically generated arcs. This is because of the difficulty
of establishing the true (gold) emotion arc. Our work, for the first time,
systematically and quantitatively evaluates automatically generated emotion
arcs. We also compare two common ways of generating emotion arcs:
Machine-Learning (ML) models and Lexicon-Only (LexO) methods. By running
experiments on 18 diverse datasets in 9 languages, we show that despite being
markedly poor at instance level emotion classification, LexO methods are highly
accurate at generating emotion arcs when aggregating information from hundreds
of instances. We also show, through experiments on six indigenous African
languages, as well as Arabic, and Spanish, that automatic translations of
English emotion lexicons can be used to generate high-quality emotion arcs in
less-resource languages. This opens up avenues for work on emotions in
languages from around the world; which is crucial for commerce, public policy,
and health research in service of speakers often left behind. Code and
resources: https://github.com/dteodore/EmotionArcs
