README.md

---
language:
- ar
- de
- en
- es
- ha
- pt
- ro
- ru
- uk
- zh
license: cc-by-4.0
dataset_info:
- config_name: arq
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: anger
    dtype: int64
  - name: disgust
    dtype: int64
  - name: fear
    dtype: int64
  - name: joy
    dtype: int64
  - name: sadness
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  - name: surprise
    dtype: int64
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    num_examples: 901
  - name: dev
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    num_examples: 100
  - name: test
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  download_size: 168878
  dataset_size: 379887.5987218572
- config_name: chn
  features:
  - name: id
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  - name: text
    dtype: string
  - name: anger
    dtype: int64
  - name: disgust
    dtype: int64
  - name: fear
    dtype: int64
  - name: joy
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  - name: sadness
    dtype: int64
  - name: surprise
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  - name: dev
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  - name: test
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  download_size: 776879
  dataset_size: 1095488.1485649897
- config_name: deu
  features:
  - name: id
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  - name: text
    dtype: string
  - name: anger
    dtype: int64
  - name: disgust
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  - name: fear
    dtype: int64
  - name: joy
    dtype: int64
  - name: sadness
    dtype: int64
  - name: surprise
    dtype: int64
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  - name: dev
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    num_examples: 200
  - name: test
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  download_size: 900359
  dataset_size: 1080084.8413043707
- config_name: eng
  features:
  - name: id
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  - name: text
    dtype: string
  - name: anger
    dtype: int64
  - name: disgust
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  - name: fear
    dtype: int64
  - name: joy
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  - name: sadness
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  - name: surprise
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    num_examples: 2768
  - name: dev
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    num_examples: 116
  - name: test
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  download_size: 384196
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- config_name: esp
  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: fear
    dtype: int64
  - name: joy
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  - name: sadness
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  - name: surprise
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  splits:
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  - name: dev
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  - name: test
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  download_size: 206706
  dataset_size: 773089.7586513865
- config_name: hau
  features:
  - name: id
    dtype: string
  - name: text
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  - name: anger
    dtype: int64
  - name: disgust
    dtype: int64
  - name: fear
    dtype: int64
  - name: joy
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  - name: sadness
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  - name: surprise
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  - name: dev
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    num_examples: 356
  - name: test
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    num_examples: 1080
  download_size: 258984
  dataset_size: 711234.0212767324
- config_name: ptbr
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: anger
    dtype: int64
  - name: disgust
    dtype: int64
  - name: fear
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  - name: joy
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  - name: sadness
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  - name: surprise
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  - name: dev
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    num_examples: 200
  - name: test
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    num_examples: 2226
  download_size: 449617
  dataset_size: 929030.096037935
- config_name: ron
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: anger
    dtype: int64
  - name: disgust
    dtype: int64
  - name: fear
    dtype: int64
  - name: joy
    dtype: int64
  - name: sadness
    dtype: int64
  - name: surprise
    dtype: int64
  splits:
  - name: train
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    num_examples: 1239
  - name: dev
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    num_examples: 123
  - name: test
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    num_examples: 1119
  download_size: 229120
  dataset_size: 495083.1493890041
- config_name: rus
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: anger
    dtype: int64
  - name: disgust
    dtype: int64
  - name: fear
    dtype: int64
  - name: joy
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  - name: sadness
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  - name: surprise
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  - name: dev
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  - name: test
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  download_size: 257485
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- config_name: ukr
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: anger
    dtype: int64
  - name: disgust
    dtype: int64
  - name: fear
    dtype: int64
  - name: joy
    dtype: int64
  - name: sadness
    dtype: int64
  - name: surprise
    dtype: int64
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  - name: dev
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    num_examples: 249
  - name: test
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    num_examples: 2234
  download_size: 380447
  dataset_size: 987556.4973841995
configs:
- config_name: arq
  data_files:
  - split: train
    path: arq/train-*
  - split: dev
    path: arq/dev-*
  - split: test
    path: arq/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: ptbr
  data_files:
  - split: train
    path: ptbr/train-*
  - split: dev
    path: ptbr/dev-*
  - split: test
    path: ptbr/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: ukr
  data_files:
  - split: train
    path: ukr/train-*
  - split: dev
    path: ukr/dev-*
  - split: test
    path: ukr/test-*
---

# BRIGHTER Emotion Intensities Dataset

This dataset contains the emotion intensities data from the BRIGHTER paper: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages.

## Dataset Description

The BRIGHTER Emotion Intensities dataset is a comprehensive multi-language emotion intensity dataset with separate configurations for each language. It represents one of the largest human-annotated emotion datasets across multiple languages, providing numerical intensity scores for emotions.

- **Total languages**: 10 languages
- **Total examples**: 41196
- **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 emotion intensities: anger, disgust, fear, joy, sadness, and surprise.

The dataset contains text in the following languages: Algerian Arabic, Mandarin Chinese, German, English, Spanish (Ecuador, Colombia, Mexico), Hausa, Portuguese (Brazil), Romanian, Russian, and Ukrainian.

## 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 |
|---------------|----------|---------------|-------------|--------------|-------|
| arq | ar | 901 | 100 | 902 | 1903 |
| chn | zh | 2642 | 200 | 2642 | 5484 |
| deu | de | 2603 | 200 | 2604 | 5407 |
| eng | en | 2768 | 116 | 2767 | 5651 |
| esp | es | 1996 | 184 | 1695 | 3875 |
| hau | ha | 2145 | 356 | 1080 | 3581 |
| ptbr | pt | 2226 | 200 | 2226 | 4652 |
| ron | ro | 1239 | 123 | 1119 | 2481 |
| rus | ru | 2220 | 343 | 650 | 3213 |
| ukr | uk | 2466 | 249 | 2234 | 4949 |

## Features

- **id**: Unique identifier for each example
- **text**: Text content to classify
- **anger**, **disgust**, **fear**, **joy**, **sadness**, **surprise**: Intensity scores for each emotion

## Dataset Characteristics

Unlike the BRIGHTER-emotion-categories dataset that provides binary labels for emotion presence, this dataset provides intensity scores on a scale, making it suitable for regression tasks or fine-grained emotion analysis.

## Usage

```python
from datasets import load_dataset

# Load all data for a specific language
eng_dataset = load_dataset("YOUR_USERNAME/BRIGHTER-emotion-intensities", "eng")

# Or load a specific split for a language
eng_train = load_dataset("YOUR_USERNAME/BRIGHTER-emotion-intensities", "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.