metadata
language:
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- ha
- hi
- id
- ig
- jv
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- pcm
- pt
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- ru
- rw
- su
- sv
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- tt
- uk
- vmw
- xh
- yo
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- zu
license: cc-by-4.0
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path: yor/dev-*
- split: test
path: yor/test-*
- config_name: zul
data_files:
- split: dev
path: zul/dev-*
- split: test
path: zul/test-*
BRIGHTER Emotion Categories Dataset
This dataset contains the emotion categories data from the BRIGHTER paper: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages.
Dataset Description
The BRIGHTER Emotion Categories dataset is a comprehensive multi-language, multi-label emotion classification dataset with separate configurations for each language. It represents one of the largest human-annotated emotion datasets across multiple languages.
- Total languages: 28 languages
- Total examples: 139595
- Splits: train, dev, test
About BRIGHTER
BRIGHTER addresses the gap in human-annotated textual emotion recognition datasets for low-resource languages. While most existing emotion datasets focus on English, BRIGHTER covers multiple languages, including many low-resource ones. The dataset was created by selecting text from various sources and having annotators label six categorical emotions: anger, disgust, fear, joy, sadness, and surprise.
The dataset contains text in the following languages: Afrikaans, Algerian Arabic, Moroccan Arabic, Mandarin Chinese, German, English, Spanish (Ecuador, Colombia, Mexico), Hausa, Hindi, Igbo, Indonesian, Javanese, Kinyarwanda, Marathi, Nigerian Pidgin, Portuguese (Brazil), Portuguese (Mozambique), Romanian, Russian, Sundanese, Swahili, Swedish, Tatar, Ukrainian, Makhuwa, Xhosa, Yoruba, and Zulu.
Language Configurations
Each language is available as a separate configuration with the following statistics:
| Original Code | ISO Code | Train Examples | Dev Examples | Test Examples | Total |
|---|---|---|---|---|---|
| afr | af | 1222 | 196 | 2130 | 3548 |
| arq | ar | 901 | 200 | 1804 | 2905 |
| ary | ar | 1608 | 534 | 1624 | 3766 |
| chn | zh | 2642 | 400 | 5284 | 8326 |
| deu | de | 2603 | 400 | 5208 | 8211 |
| eng | en | 2768 | 232 | 5534 | 8534 |
| esp | es | 1996 | 368 | 3390 | 5754 |
| hau | ha | 2145 | 712 | 2160 | 5017 |
| hin | hi | 2556 | 200 | 2020 | 4776 |
| ibo | ig | 2880 | 958 | 2888 | 6726 |
| ind | id | 0 | 156 | 851 | 1007 |
| jav | jv | 0 | 151 | 837 | 988 |
| kin | rw | 2451 | 814 | 2462 | 5727 |
| mar | mr | 2415 | 200 | 2000 | 4615 |
| pcm | pcm | 3728 | 1240 | 3740 | 8708 |
| ptbr | pt | 2226 | 400 | 4452 | 7078 |
| ptmz | pt | 1546 | 514 | 1552 | 3612 |
| ron | ro | 1241 | 246 | 2238 | 3725 |
| rus | ru | 2679 | 398 | 2000 | 5077 |
| sun | su | 924 | 398 | 1852 | 3174 |
| swa | sw | 3307 | 1102 | 3312 | 7721 |
| swe | sv | 1187 | 400 | 2376 | 3963 |
| tat | tt | 1000 | 400 | 2000 | 3400 |
| ukr | uk | 2466 | 498 | 4468 | 7432 |
| vmw | vmw | 1551 | 516 | 1554 | 3621 |
| xho | xh | 0 | 682 | 1594 | 2276 |
| yor | yo | 2992 | 994 | 3000 | 6986 |
| zul | zu | 0 | 875 | 2047 | 2922 |
Features
- id: Unique identifier for each example
- text: Text content to classify
- anger, disgust, fear, joy, sadness, surprise: Presence of emotion
- emotions: List of emotions present in the text
Dataset Characteristics
This dataset provides binary labels for emotion presence, making it suitable for multi-label classification tasks. For regression tasks or fine-grained emotion analysis, please see the companion BRIGHTER-emotion-intensities dataset.
Usage
from datasets import load_dataset
# Load all data for a specific language
eng_dataset = load_dataset("YOUR_USERNAME/BRIGHTER-emotion-categories", "eng")
# Or load a specific split for a language
eng_train = load_dataset("YOUR_USERNAME/BRIGHTER-emotion-categories", "eng", split="train")
Citation
If you use this dataset, please cite the following papers:
@misc{muhammad2025brighterbridginggaphumanannotated,
title={BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages},
author={Shamsuddeen Hassan Muhammad and Nedjma Ousidhoum and Idris Abdulmumin and Jan Philip Wahle and Terry Ruas and Meriem Beloucif and Christine de Kock and Nirmal Surange and Daniela Teodorescu and Ibrahim Said Ahmad and David Ifeoluwa Adelani and Alham Fikri Aji and Felermino D. M. A. Ali and Ilseyar Alimova and Vladimir Araujo and Nikolay Babakov and Naomi Baes and Ana-Maria Bucur and Andiswa Bukula and Guanqun Cao and Rodrigo Tufiño and Rendi Chevi and Chiamaka Ijeoma Chukwuneke and Alexandra Ciobotaru and Daryna Dementieva and Murja Sani Gadanya and Robert Geislinger and Bela Gipp and Oumaima Hourrane and Oana Ignat and Falalu Ibrahim Lawan and Rooweither Mabuya and Rahmad Mahendra and Vukosi Marivate and Andrew Piper and Alexander Panchenko and Charles Henrique Porto Ferreira and Vitaly Protasov and Samuel Rutunda and Manish Shrivastava and Aura Cristina Udrea and Lilian Diana Awuor Wanzare and Sophie Wu and Florian Valentin Wunderlich and Hanif Muhammad Zhafran and Tianhui Zhang and Yi Zhou and Saif M. Mohammad},
year={2025},
eprint={2502.11926},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.11926},
}
@misc{muhammad2025semeval2025task11bridging,
title={SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection},
author={Shamsuddeen Hassan Muhammad and Nedjma Ousidhoum and Idris Abdulmumin and Seid Muhie Yimam and Jan Philip Wahle and Terry Ruas and Meriem Beloucif and Christine De Kock and Tadesse Destaw Belay and Ibrahim Said Ahmad and Nirmal Surange and Daniela Teodorescu and David Ifeoluwa Adelani and Alham Fikri Aji and Felermino Ali and Vladimir Araujo and Abinew Ali Ayele and Oana Ignat and Alexander Panchenko and Yi Zhou and Saif M. Mohammad},
year={2025},
eprint={2503.07269},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.07269},
}
License
This dataset is licensed under CC-BY 4.0.