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 low-resource languages. We release our model to the community so that interested researchers can directly use it.
Anthology ID:
2022.wassa-1.18
Volume:
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
195–203
Language:
URL:
https://aclanthology.org/2022.wassa-1.18
DOI:
10.18653/v1/2022.wassa-1.18
Bibkey:
Cite (ACL):
Federico Bianchi, Debora Nozza, and Dirk Hovy. 2022. XLM-EMO: Multilingual Emotion Prediction in Social Media Text. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 195–203, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
XLM-EMO: Multilingual Emotion Prediction in Social Media Text (Bianchi et al., WASSA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.wassa-1.18.pdf
Code
 milanlproc/xlm-emo