---
license: mit
task_categories:
- text-classification
language:
- swa
tags:
- emotion
- african-languages
- nlp
- text-classification
size_categories:
- 1M<n<10M
---

# Swahili Emotion Analysis Corpus

## Dataset Description

This dataset contains emotion-labeled text data in Swahili 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**: 2,000,000
- **Joy**: 147146 (7.4%)
- **Sadness**: 114445 (5.7%)
- **Anger**: 125076 (6.3%)
- **Fear**: 89559 (4.5%)
- **Surprise**: 105550 (5.3%)
- **Disgust**: 162737 (8.1%)
- **Neutral**: 1255487 (62.8%)

## Dataset Structure

### Data Fields

- **Text Column**: Contains the original text in Swahili
- **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

```python
from datasets import load_dataset

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

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

## Citation

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

```bibtex
@dataset{swahili_emotions_corpus,
  title={Swahili Emotions Corpus},
  author={Mich-Seth Owusu},
  year={2025},
  url={https://huggingface.co/datasets/michsethowusu/swahili-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