michsethowusu/twi-sentiments-corpus

Twi Sentiment Corpus

Dataset Description

This dataset contains sentiment-labeled text data in Twi for binary sentiment classification (Positive/Negative). Sentiments are extracted and processed from the English meanings of the sentences using DistilBERT for sentiment classification. The dataset is part of a larger collection of African language sentiment analysis resources.

Dataset Statistics

• Total samples: 432,647
• Positive sentiment: 249237 (57.6%)
• Negative sentiment: 183410 (42.4%)

Dataset Structure

Data Fields

• Text Column: Contains the original text in Twi
• sentiment: Sentiment label (Positive or Negative only)

Data Splits

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

Data Processing

The sentiment labels were generated using:

• Model: distilbert-base-uncased-finetuned-sst-2-english
• Processing: Batch processing with optimization for efficiency
• Deduplication: Duplicate entries were removed based on text content
• Filtering: Only Positive and Negative sentiments retained for binary classification

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("michsethowusu/twi-sentiments-corpus")

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

# Check sentiment distribution
from collections import Counter
sentiments = [item['sentiment'] for item in dataset['train']]
print(Counter(sentiments))

Use Cases

This dataset is ideal for:

• Binary sentiment classification tasks
• Training sentiment analysis models for Twi
• Cross-lingual sentiment analysis research
• African language NLP model development

Citation

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

@dataset{twi_sentiments_corpus,
  title={Twi Sentiment Corpus},
  author={Mich-Seth Owusu},
  year={2025},
  url={https://huggingface.co/datasets/michsethowusu/twi-sentiments-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: 2025-07-02 Processing Pipeline: Automated sentiment analysis using HuggingFace Transformers Quality Control: Deduplication, batch processing optimizations, and binary sentiment filtering applied