brighter-dataset/BRIGHTER-emotion-intensities

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.