README.md

metadata

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

Amharic-english Emotion Analysis Corpus

Dataset Description

This dataset contains emotion-labeled text data in Amharic-english 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: 141104 (7.1%)
• Sadness: 97367 (4.9%)
• Anger: 111968 (5.6%)
• Fear: 83797 (4.2%)
• Surprise: 81250 (4.1%)
• Disgust: 151441 (7.6%)
• Neutral: 1333073 (66.7%)

Dataset Structure

Data Fields

• Text Column: Contains the original text in Amharic-english
• 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/amharic-english-emotions-corpus")

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

Citation

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

@dataset{amharic-english_emotions_corpus,
  title={Amharic-english Emotions Corpus},
  author={Mich-Seth Owusu},
  year={2025},
  url={https://huggingface.co/datasets/michsethowusu/amharic-english-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