Datasets:
metadata
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
dtype: int64
- name: surprise
dtype: int64
splits:
- name: train
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num_examples: 901
- name: dev
num_bytes: 19159.729599227427
num_examples: 100
- name: test
num_bytes: 182325.91774094536
num_examples: 902
download_size: 168878
dataset_size: 379887.5987218572
- config_name: chn
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: 2642
- name: dev
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num_examples: 200
- name: test
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num_examples: 2642
download_size: 776879
dataset_size: 1095488.1485649897
- config_name: deu
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:
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num_examples: 2603
- name: dev
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num_examples: 200
- name: test
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num_examples: 2604
download_size: 900359
dataset_size: 1080084.8413043707
- config_name: eng
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:
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num_examples: 2768
- name: dev
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num_examples: 116
- name: test
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num_examples: 2767
download_size: 384196
dataset_size: 1129609.423755175
- config_name: esp
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:
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- name: dev
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num_examples: 184
- name: test
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num_examples: 1695
download_size: 206706
dataset_size: 773089.7586513865
- config_name: hau
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
splits:
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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
dtype: int64
- name: joy
dtype: int64
- name: sadness
dtype: int64
- name: surprise
dtype: int64
splits:
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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:
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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
dtype: int64
- name: sadness
dtype: int64
- name: surprise
dtype: int64
splits:
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- name: dev
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num_examples: 343
- name: test
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num_examples: 650
download_size: 257485
dataset_size: 636675.4649143497
- 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
splits:
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num_examples: 2466
- name: dev
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num_examples: 249
- name: test
num_bytes: 451569.9559127183
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
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.