Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis
This work addresses the lack of evaluation for emotion arcs, enabling broader application in commerce, public policy, and health research for speakers of under-resourced languages.
The paper systematically evaluates automatically generated emotion arcs across languages, showing that lexicon-only methods are highly accurate at generating arcs when aggregating hundreds of instances, and that automatic translations of English lexicons can produce high-quality arcs in less-resourced languages like indigenous African ones.
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