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--- |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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language: |
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- uk |
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tags: |
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- emotion |
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pretty_name: emobench-ua |
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size_categories: |
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- 1K<n<10K |
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--- |
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# EmoBench-UA: Emotions Detection Dataset in Ukrainian Texts |
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<img alt="EmoBench-UA" style="width: 75%; height: 75%" src="intro_logo.png"> |
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**EmoBench-UA**: the first of its kind emotions detection dataset in Ukrainian texts. This dataset covers the detection of basic emotions: Joy, Anger, Fear, Disgust, Surprise, Sadness, or None. |
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Any text can contain any amount of emotion -- only one, several, or none at all. The texts with *None* emotions are the ones where the labels per emotions classes are 0. |
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*Binary*: specifically this dataset contains binary labels indicating simply presence of any emotion in the text. |
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## Data Collection |
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The data collection was done via [Toloka.ai](https://toloka.ai}{https://toloka.ai) crowdsourcing platform. |
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For original Ukrainian texts, we used the opensourced [corpus of Ukrainian tweets](https://github.com/kateryna-bobrovnyk/ukr-twi-corpus). |
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Firstly, we did data pre-filtering: |
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**Length** We applied a length-based filter, discarding texts that were too short (N words < 5), as such samples often consist of hashtags or other non-informative tokens. Similarly, overly long texts (N words >= 50) were excluded, as longer sequences tend to obscure the central meaning and make it challenging to accurately identify the expressed emotions. |
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**Toxicity** While toxic texts can carry quite strong emotions, to ensure annotators well-being and general appropriateness of our corpus, we filtered out too toxic instances using our opensourced [toxicity classifier](https://huggingface.co/ukr-detect/ukr-toxicity-classifier). |
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**Emotional Texts Pre-selection** To avoid an excessive imbalance toward emotionless texts, we performed a pre-selection step aimed at identifying texts likely to express emotions. Specifically, we applied the English emotion classifier [DistillRoBERTa-Emo-EN](https://huggingface.co/michellejieli/emotion_text_classifier) on translated Ukrainian texts with [NLLB](https://huggingface.co/facebook/nllb-200-distilled-600M) model. |
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Then, we utilized several control strategies to ensure the quality of the data: |
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* annotators were native Ukrainian speakers; |
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* annotators were obliged to finish training and examination before being allowed to perform annotation; |
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* annotators were permanently banned if they submitted the last three task pages in under 15 seconds each, indicating low engagement; |
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* a one-day ban was triggered if three consecutive pages were skipped; |
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* annotators were asked to take a 30-minute break after completing 25 consecutive pages; |
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* control tasks were randomly injected to check. |
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Finally, each sample were annotated by 5 annotators. We took for datasets only instances with 90% confidence score. |
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## Splits Statistics |
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Overall, the datasets contains of 4949 labelled instances. Krippendorff's alpha annotation agreement score is 0.85. |
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Then, we partitioned the dataset into fixed train/development/test subsets following a 50/5/45% split ratio. |
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<img alt="Splits Statistic" style="width: 50%; height: 50%" src="data_stats.png"> |
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## Citation |
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If you would like to acknowledge our work, please, cite the following manuscript: |
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``` |
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@article{dementieva2025emobench, |
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title={EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian}, |
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author={Dementieva, Daryna and Babakov, Nikolay and Fraser, Alexander}, |
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journal={arXiv preprint arXiv:2505.23297}, |
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year={2025} |
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} |
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``` |
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## Contacts |
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[Daryna Dementieva](https://huggingface.co/dardem), [Nikolay Babakov](https://huggingface.co/NiGuLa) |
