# BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages

arXiv: https://arxiv.org/abs/2502.11926 · HTML: https://mlsanity.com/paper/2502.11926/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 59 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [POTATO: The Portable Text Annotation Tool](https://mlsanity.com/paper/2212.08620/) (2022)
- [Multilingual Language Models are not Multicultural: A Case Study in Emotion](https://mlsanity.com/paper/2307.01370/) (2023)
- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](https://mlsanity.com/paper/2011.01612/) (2020)
- [Best Practices in the Creation and Use of Emotion Lexicons](https://mlsanity.com/paper/2210.07206/) (2022)
- [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024)
- [Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text](https://mlsanity.com/paper/2005.11882/) (2020)
- [Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling](https://mlsanity.com/paper/1712.01741/) (2017)
- [No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages](https://mlsanity.com/paper/2411.03769/) (2024)
- [Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer](https://mlsanity.com/paper/2103.12360/) (2021)
- [Emotion Detection in Text: a Review](https://mlsanity.com/paper/1806.00674/) (2018)
- [WorryWords: Norms of Anxiety Association for over 44k English Words](https://mlsanity.com/paper/2411.03966/) (2024)
- [Emotion Granularity from Text: An Aggregate-Level Indicator of Mental Health](https://mlsanity.com/paper/2403.02281/) (2024)

## Built on by (most cited first, 27 of 27)

- [AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking in Large Language Models](https://mlsanity.com/paper/2505.17312/) (2025)
- [SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection](https://mlsanity.com/paper/2503.07269/) (2025)
- [AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text](https://mlsanity.com/paper/2503.18247/) (2025)
- [AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages](https://mlsanity.com/paper/2510.23896/) (2025)
- [When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges](https://mlsanity.com/paper/2605.26046/) (2026)
- [HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing](https://mlsanity.com/paper/2505.14311/) (2025)
- [Team A at SemEval-2025 Task 11: Breaking Language Barriers in Emotion Detection with Multilingual Models](https://mlsanity.com/paper/2502.19856/) (2025)
- [Lotus at SemEval-2025 Task 11: RoBERTa with Llama-3 Generated Explanations for Multi-Label Emotion Classification](https://mlsanity.com/paper/2502.19935/) (2025)
- [Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages](https://mlsanity.com/paper/2503.18253/) (2025)
- [DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2601.23022/) (2026)
- [JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models](https://mlsanity.com/paper/2505.13244/) (2025)
- [University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection](https://mlsanity.com/paper/2505.16460/) (2025)
- [EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian](https://mlsanity.com/paper/2505.23297/) (2025)
- [Empaths at SemEval-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction](https://mlsanity.com/paper/2506.04409/) (2025)
- [HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection](https://mlsanity.com/paper/2506.16388/) (2025)
- [Few-shot text-based emotion detection](https://mlsanity.com/paper/2507.05918/) (2025)
- [PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts](https://mlsanity.com/paper/2507.08499/) (2025)
- [Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss](https://mlsanity.com/paper/2507.11384/) (2025)
- [Chinchunmei at SemEval-2025 Task 11: Boosting the Large Language Model's Capability of Emotion Perception using Contrastive Learning](https://mlsanity.com/paper/2507.15714/) (2025)
- [CSIRO-LT at SemEval-2025 Task 11: Adapting LLMs for Emotion Recognition for Multiple Languages](https://mlsanity.com/paper/2508.01161/) (2025)
- [Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning](https://mlsanity.com/paper/2509.20315/) (2025)
- [ViGoEmotions: A Benchmark Dataset For Fine-grained Emotion Detection on Vietnamese Texts](https://mlsanity.com/paper/2602.08371/) (2026)
- [PsihoRo: Depression and Anxiety Romanian Text Corpus](https://mlsanity.com/paper/2602.18324/) (2026)
- [A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development](https://mlsanity.com/paper/2605.22828/) (2026)
- [MTEB-BR: A Text Embedding Benchmark for Brazilian Portuguese](https://mlsanity.com/paper/2607.04581/) (2026)
- [Chiaroscuro for Emotions: A Contrastive Emotion Benchmark Grounded in Appraisal Theory](https://mlsanity.com/paper/2609.03394/) (2026)
- [Disentangling Topology and Diversity in Multi-Agent LLMs for Multilingual Low-Resource Emotion Detection](https://mlsanity.com/paper/2609.14570/) (2026)

## Abstract (arXiv)

People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition--an umbrella term for several NLP tasks--impacts various applications within NLP and beyond, most work in this area has focused on high-resource languages. This has led to significant disparities in research efforts and proposed solutions, particularly for under-resourced languages, which often lack high-quality annotated datasets. In this paper, we present BRIGHTER--a collection of multi-labeled, emotion-annotated datasets in 28 different languages and across several domains. BRIGHTER primarily covers low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers. We highlight the challenges related to the data collection and annotation processes, and then report experimental results for monolingual and crosslingual multi-label emotion identification, as well as emotion intensity recognition. We analyse the variability in performance across languages and text domains, both with and without the use of LLMs, and show that the BRIGHTER datasets represent a meaningful step towards addressing the gap in text-based emotion recognition.