XLM-EMO: Multilingual Emotion Prediction in Social Media Text
Federico Bianchi, Debora Nozza, Dirk Hovy
Abstract
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of lowresource languages. We release our model to the community so that interested researchers can directly use it.
Topics & Concepts
Computer scienceSocial mediaContext (archaeology)Emotion detectionData scienceResource (disambiguation)World Wide WebNatural language processingOnline communityArtificial intelligenceEmotion recognitionComputer networkPaleontologyBiologySentiment Analysis and Opinion MiningComplex Network Analysis TechniquesTopic Modeling