README.md

---
language:
- af
- ar
- de
- en
- es
- ha
- hi
- id
- ig
- jv
- mr
- pcm
- pt
- ro
- ru
- rw
- su
- sv
- sw
- tt
- uk
- vmw
- xh
- yo
- zh
- zu
license: cc-by-4.0
dataset_info:
- config_name: afr
  features:
  - name: id
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  - name: text
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  - name: anger
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  - name: disgust
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  - name: joy
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  - name: sadness
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  - name: surprise
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  - name: emotions
    sequence: string
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    num_examples: 1222
  - name: dev
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    num_examples: 196
  - name: test
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    num_examples: 2130
  download_size: 253496
  dataset_size: 738846.9169837649
- config_name: arq
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  - name: joy
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  - name: sadness
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  - name: surprise
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  - name: emotions
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  - name: dev
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  - name: test
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  download_size: 242601
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- config_name: ary
  features:
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  - name: disgust
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  - name: joy
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  - name: sadness
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  - name: surprise
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  - name: emotions
    sequence: string
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  - name: dev
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    num_examples: 534
  - name: test
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  download_size: 324284
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- config_name: chn
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  - name: emotions
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  - name: dev
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    num_examples: 400
  - name: test
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    num_examples: 5284
  download_size: 1145616
  dataset_size: 1734324.8717409424
- config_name: deu
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  - name: emotions
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  - name: dev
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    num_examples: 400
  - name: test
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    num_examples: 5208
  download_size: 1298256
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- config_name: eng
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  - name: dev
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  - name: test
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  download_size: 568875
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- config_name: esp
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  - name: test
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    num_examples: 3390
  download_size: 284253
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- config_name: hau
  features:
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  - name: dev
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- config_name: hin
  features:
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    num_examples: 2020
  download_size: 477173
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- config_name: ibo
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    num_examples: 2888
  download_size: 327624
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- config_name: ind
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  download_size: 77237
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- config_name: jav
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  splits:
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    num_examples: 151
  - name: test
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  download_size: 83583
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- config_name: kin
  features:
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  splits:
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  - name: dev
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  - name: test
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  download_size: 542662
  dataset_size: 1185384.260087552
- config_name: mar
  features:
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  download_size: 446771
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- config_name: pcm
  features:
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  splits:
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- config_name: ptbr
  features:
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  download_size: 645789
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- config_name: ptmz
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- config_name: ron
  features:
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- config_name: rus
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- config_name: sun
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- config_name: swa
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- config_name: swe
  features:
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- config_name: tat
  features:
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  download_size: 253550
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- config_name: ukr
  features:
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  download_size: 541083
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- config_name: vmw
  features:
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  - name: test
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- config_name: xho
  features:
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  - name: test
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- config_name: yor
  features:
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- config_name: zul
  features:
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  - name: test
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    num_examples: 2047
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  dataset_size: 594313.7871657871
configs:
- config_name: afr
  data_files:
  - split: train
    path: afr/train-*
  - split: dev
    path: afr/dev-*
  - split: test
    path: afr/test-*
- config_name: arq
  data_files:
  - split: train
    path: arq/train-*
  - split: dev
    path: arq/dev-*
  - split: test
    path: arq/test-*
- config_name: ary
  data_files:
  - split: train
    path: ary/train-*
  - split: dev
    path: ary/dev-*
  - split: test
    path: ary/test-*
- config_name: chn
  data_files:
  - split: train
    path: chn/train-*
  - split: dev
    path: chn/dev-*
  - split: test
    path: chn/test-*
- config_name: deu
  data_files:
  - split: train
    path: deu/train-*
  - split: dev
    path: deu/dev-*
  - split: test
    path: deu/test-*
- config_name: eng
  data_files:
  - split: train
    path: eng/train-*
  - split: dev
    path: eng/dev-*
  - split: test
    path: eng/test-*
- config_name: esp
  data_files:
  - split: train
    path: esp/train-*
  - split: dev
    path: esp/dev-*
  - split: test
    path: esp/test-*
- config_name: hau
  data_files:
  - split: train
    path: hau/train-*
  - split: dev
    path: hau/dev-*
  - split: test
    path: hau/test-*
- config_name: hin
  data_files:
  - split: train
    path: hin/train-*
  - split: dev
    path: hin/dev-*
  - split: test
    path: hin/test-*
- config_name: ibo
  data_files:
  - split: train
    path: ibo/train-*
  - split: dev
    path: ibo/dev-*
  - split: test
    path: ibo/test-*
- config_name: ind
  data_files:
  - split: dev
    path: ind/dev-*
  - split: test
    path: ind/test-*
- config_name: jav
  data_files:
  - split: dev
    path: jav/dev-*
  - split: test
    path: jav/test-*
- config_name: kin
  data_files:
  - split: train
    path: kin/train-*
  - split: dev
    path: kin/dev-*
  - split: test
    path: kin/test-*
- config_name: mar
  data_files:
  - split: train
    path: mar/train-*
  - split: dev
    path: mar/dev-*
  - split: test
    path: mar/test-*
- config_name: pcm
  data_files:
  - split: train
    path: pcm/train-*
  - split: dev
    path: pcm/dev-*
  - split: test
    path: pcm/test-*
- config_name: ptbr
  data_files:
  - split: train
    path: ptbr/train-*
  - split: dev
    path: ptbr/dev-*
  - split: test
    path: ptbr/test-*
- config_name: ptmz
  data_files:
  - split: train
    path: ptmz/train-*
  - split: dev
    path: ptmz/dev-*
  - split: test
    path: ptmz/test-*
- config_name: ron
  data_files:
  - split: train
    path: ron/train-*
  - split: dev
    path: ron/dev-*
  - split: test
    path: ron/test-*
- config_name: rus
  data_files:
  - split: train
    path: rus/train-*
  - split: dev
    path: rus/dev-*
  - split: test
    path: rus/test-*
- config_name: sun
  data_files:
  - split: train
    path: sun/train-*
  - split: dev
    path: sun/dev-*
  - split: test
    path: sun/test-*
- config_name: swa
  data_files:
  - split: train
    path: swa/train-*
  - split: dev
    path: swa/dev-*
  - split: test
    path: swa/test-*
- config_name: swe
  data_files:
  - split: train
    path: swe/train-*
  - split: dev
    path: swe/dev-*
  - split: test
    path: swe/test-*
- config_name: tat
  data_files:
  - split: train
    path: tat/train-*
  - split: dev
    path: tat/dev-*
  - split: test
    path: tat/test-*
- config_name: ukr
  data_files:
  - split: train
    path: ukr/train-*
  - split: dev
    path: ukr/dev-*
  - split: test
    path: ukr/test-*
- config_name: vmw
  data_files:
  - split: train
    path: vmw/train-*
  - split: dev
    path: vmw/dev-*
  - split: test
    path: vmw/test-*
- config_name: xho
  data_files:
  - split: dev
    path: xho/dev-*
  - split: test
    path: xho/test-*
- config_name: yor
  data_files:
  - split: train
    path: yor/train-*
  - split: dev
    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

```python
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.