Universal Joy A Data Set and Results for Classifying Emotions Across Languages

Sotiris Lamprinidis, Federico Bianchi, Daniel Hardt, Dirk Hovy

Abstract
While emotions are universal aspects of human psychology, they are expressed differently across different languages and cultures. We introduce a new data set of over 530k anonymized public Facebook posts across 18 languages, labeled with five different emotions. Using multilingual BERT embeddings, we show that emotions can be reliably inferred both within and across languages. Zero-shot learning produces promising results for low-resource languages. Following established theories of basic emotions, we provide a detailed analysis of the possibilities and limits of cross-lingual emotion classification. We find that structural and typological similarity between languages facilitates cross-lingual learning, as well as linguistic diversity of training data. Our results suggest that there are commonalities underlying the expression of emotion in different languages. We publicly release the anonymized data for future research.
Anthology ID:
2021.wassa-1.7
Volume:
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
April
Year:
2021
Address:
Online
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
62–75
Language:
URL:
https://aclanthology.org/2021.wassa-1.7
DOI:
Bibkey:
Cite (ACL):
Sotiris Lamprinidis, Federico Bianchi, Daniel Hardt, and Dirk Hovy. 2021. Universal Joy A Data Set and Results for Classifying Emotions Across Languages. In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 62–75, Online. Association for Computational Linguistics.
Cite (Informal):
Universal Joy A Data Set and Results for Classifying Emotions Across Languages (Lamprinidis et al., WASSA 2021)
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