metadata

license: mit
task_categories:
  - text-classification
language:
  - kbp
tags:
  - emotion
  - african-languages
  - nlp
  - text-classification
size_categories:
  - 10K<n<100K

Kabiye Emotion Analysis Corpus

Dataset Description

This dataset contains emotion-labeled text data in Kabiye for emotion classification (joy, sadness, anger, fear, surprise, disgust, neutral). Emotions were extracted and processed from the English meanings of the sentences using the model j-hartmann/emotion-english-distilroberta-base. The dataset is part of a larger collection of African language emotion analysis resources.

Dataset Statistics

Total samples: 22,804
Joy: 1332 (5.8%)
Sadness: 928 (4.1%)
Anger: 895 (3.9%)
Fear: 773 (3.4%)
Surprise: 937 (4.1%)
Disgust: 1368 (6.0%)
Neutral: 16571 (72.7%)

Dataset Structure

Data Fields

Text Column: Contains the original text in Kabiye
emotion: Emotion label (joy, sadness, anger, fear, surprise, disgust, neutral)

Data Splits

This dataset contains a single split with all the processed data.

Data Processing

The emotion labels were generated using:

Model: j-hartmann/emotion-english-distilroberta-base
Processing: Batch processing with optimization for efficiency
Deduplication: Duplicate entries were removed based on text content

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("michsethowusu/kabiye-emotions-corpus")

# Access the data
print(dataset['train'][0])

Citation

If you use this dataset in your research, please cite:

@dataset{kabiye_emotions_corpus,
  title={Kabiye Emotions Corpus},
  author={Mich-Seth Owusu},
  year={2025},
  url={https://huggingface.co/datasets/michsethowusu/kabiye-emotions-corpus}
}

License

This dataset is released under the MIT License.

Contact

For questions or issues regarding this dataset, please open an issue on the dataset repository.

Dataset Creation

Date: 2025-07-04 Processing Pipeline: Automated emotion analysis using HuggingFace Transformers Quality Control: Deduplication and batch processing optimizations applied