Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis
Submission Type: Regular Long Paper
Submission Track: Multilinguality and Linguistic Diversity
Submission Track 2: Sentiment Analysis, Stylistic Analysis, and Argument Mining
Keywords: Emotion Arcs, Sentiment Analysis, Low-Resource NLP, Multilingual, Emotion Lexicons
TL;DR: We for the first time evaluate generated emotion arcs--which are widely used in industry and research--across languages (in English, middle- and low-resource languages), demonstrating that simple and interpretable methods achieve high correlations.
Abstract: 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
Submission Number: 4703
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