--- 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 num_bytes: 178401.95138168443 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 num_bytes: 523127.5866264265 num_examples: 2642 - name: dev num_bytes: 38319.45919845485 num_examples: 200 - name: test num_bytes: 534041.1027401083 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: - name: train num_bytes: 515405.4155899274 num_examples: 2603 - name: dev num_bytes: 38319.45919845485 num_examples: 200 - name: test num_bytes: 526359.9665159886 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: - name: train num_bytes: 548076.1392058851 num_examples: 2768 - name: dev num_bytes: 22225.286335103814 num_examples: 116 - name: test num_bytes: 559307.998214186 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: - name: train num_bytes: 395216.75356031314 num_examples: 1996 - name: dev num_bytes: 35253.902462578466 num_examples: 184 - name: test num_bytes: 342619.1026284949 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 dtype: int64 - name: surprise dtype: int64 splits: - name: train num_bytes: 424719.4070074507 num_examples: 2145 - name: dev num_bytes: 68208.63737324964 num_examples: 356 - name: test num_bytes: 218305.97689603214 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: - name: train num_bytes: 440757.7622371027 num_examples: 2226 - name: dev num_bytes: 38319.45919845485 num_examples: 200 - name: test num_bytes: 449952.87460237736 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 num_bytes: 245327.43369801 num_examples: 1239 - name: dev num_bytes: 23566.467407049735 num_examples: 123 - name: test num_bytes: 226189.2482839444 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: - name: train num_bytes: 439569.73592379515 num_examples: 2220 - name: dev num_bytes: 65717.87252535007 num_examples: 343 - name: test num_bytes: 131387.85646520453 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: - name: train num_bytes: 488278.8147694049 num_examples: 2466 - name: dev num_bytes: 47707.72670207629 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 ```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.