# Cross-lingual Emotion Intensity Prediction

arXiv: https://arxiv.org/abs/2004.04103 · HTML: https://mlsanity.com/paper/2004.04103/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Modeling the Severity of Complaints in Social Media](https://mlsanity.com/paper/2103.12428/) (2021)
- [Team A at SemEval-2025 Task 11: Breaking Language Barriers in Emotion Detection with Multilingual Models](https://mlsanity.com/paper/2502.19856/) (2025)
- [Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages](https://mlsanity.com/paper/2503.18253/) (2025)

## Abstract (arXiv)

Emotion intensity prediction determines the degree or intensity of an emotion that the author expresses in a text, extending previous categorical approaches to emotion detection. While most previous work on this topic has concentrated on English texts, other languages would also benefit from fine-grained emotion classification, preferably without having to recreate the amount of annotated data available in English in each new language. Consequently, we explore cross-lingual transfer approaches for fine-grained emotion detection in Spanish and Catalan tweets. To this end we annotate a test set of Spanish and Catalan tweets using Best-Worst scaling. We compare six cross-lingual approaches, e.g., machine translation and cross-lingual embeddings, which have varying requirements for parallel data -- from millions of parallel sentences to completely unsupervised. The results show that on this data, methods with low parallel-data requirements perform surprisingly better than methods that use more parallel data, which we explain through an in-depth error analysis. We make the dataset and the code available at urlhttps://github.com/jerbarnes/fine-grainedcross-lingualemotion