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---
license: mit
task_categories:
- text-classification
language:
- tso
tags:
- emotion
- african-languages
- nlp
- text-classification
size_categories:
- 100K<n<1M
---
# Tsonga Emotion Analysis Corpus
## Dataset Description
This dataset contains emotion-labeled text data in Tsonga 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**: 255,067
- **Joy**: 24545 (9.6%)
- **Sadness**: 16964 (6.7%)
- **Anger**: 15509 (6.1%)
- **Fear**: 12104 (4.7%)
- **Surprise**: 13926 (5.5%)
- **Disgust**: 21185 (8.3%)
- **Neutral**: 150834 (59.1%)
## Dataset Structure
### Data Fields
- **Text Column**: Contains the original text in Tsonga
- **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/tsonga-emotions-corpus")
# Access the data
print(dataset['train'][0])
```
## Citation
If you use this dataset in your research, please cite:
```bibtex
@dataset{tsonga_emotions_corpus,
title={Tsonga Emotions Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/tsonga-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
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