Datasets:

mteb
/

EmotionAnalysis

annotations_creators:
  - human-annotated
language:
  - afr
  - amh
  - arq
  - ary
  - cdo
  - deu
  - eng
  - gaz
  - hau
  - hin
  - ibo
  - ind
  - jav
  - kin
  - mar
  - pcm
  - ron
  - rus
  - som
  - spa
  - sun
  - swa
  - swe
  - tat
  - tir
  - ukr
  - vmw
  - xho
  - yor
  - zul
license: cc-by-4.0
multilinguality: multilingual
source_datasets:
  - llama-lang-adapt/EmotionAnalysisFinal
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
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tags:
  - mteb
  - text

EmotionAnalysisPlus

An MTEB dataset

Massive Text Embedding Benchmark

Multi-label emotion classification dataset for 28 languages released with the BRIGHTER project and SemEval-2025 Task 11.


Task category	t2c
Domains	Social, Written
Reference	https://github.com/emotion-analysis-project/SemEval2025-Task11

Source datasets:

llama-lang-adapt/EmotionAnalysisFinal

Dataset Preparation in MTEB

This repository is a staging copy of llama-lang-adapt/EmotionAnalysisFinal for MTEB. The intended long-term canonical benchmark copy is mteb/EmotionAnalysis.

Transformations

Converted per-emotion indicator columns into the MTEB multi-label format: label: list[int]
Preserved the task-facing subset names, including gaz and swh, while sourcing from the original Hub configs
Backfilled a train split where missing for benchmark compatibility before cleaning
Applied dataset cleaning before upload to reduce duplicates and copied-train overlap in the staging copy

Label Schema

0: anger
1: disgust
2: fear
3: joy
4: sadness
5: surprise

Splits and subsets

Language-specific configs from the benchmark task are preserved
The staged copy includes the cleaned train/validation/test-style splits used by EmotionAnalysisPlus

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_task("EmotionAnalysisPlus")
evaluator = mteb.MTEB([task])

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repository.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.



@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("EmotionAnalysisPlus")

desc_stats = task.metadata.descriptive_stats

{
    "validation": {
        "num_samples": 9913,
        "number_texts_intersect_with_train": 9912,
        "text_statistics": {
            "total_text_length": 913579,
            "min_text_length": 6,
            "average_text_length": 92.15968929688287,
            "max_text_length": 2022,
            "unique_texts": 9912
        },
        "image_statistics": null,
        "audio_statistics": null,
        "label_statistics": {
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}

This dataset card was automatically generated using MTEB