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metadata
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

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:

@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