gospelgit/African-Languages_Sentiments

African Languages Sentiment Dataset (Hausa, Yorùbá, Swahili)

A stitched multi-source sentiment classification dataset combining three independently collected sentiment corpora for Hausa, Yorùbá, and Swahili, built for the Adaption Labs AutoScientist Challenge (Language category).

Companion model: fine-tuned weights trained on the adapted version of this dataset via AutoScientist are released separately at gospelgit/African-Languages-Sentiment-Classifier(https://huggingface.co/gospelgit/African-Languages-Sentiment-Classifier).

File guide for this repo:

• train.csv / validation.csv / test.csv — the original, clean, human-labeled dataset described in this card (46,725 rows, 70/15/15 split). Use these files if you want the source data for your own training pipeline.

Why this dataset

Existing sentiment resources for these languages are dominated by a single domain (Twitter). This dataset combines three different sources across three different domains to reduce domain overfitting and give a more robust sentiment signal:

Source	Domain	Languages	Rows
AfriSenti	Twitter	Hausa, Yorùbá, Swahili	40,290
NollySenti	Nollywood movie reviews (human-translated)	Hausa, Yorùbá	2,510
Neurotech-HQ Swahili	Social media / product reviews (back-translated)	Swahili	3,925

Dataset structure

• text: the raw text (tweet, movie review, or social media comment)
• label: one of positive, negative, neutral (single unified label column)
• language: hausa, yoruba, or swahili
• source: which of the three original datasets the row came from
• domain: twitter, movie_review, or social_media_reviews
• split: train, validation, or test

Splits

All three languages use an identical 70 / 15 / 15 train/validation/test split, stratified by label, computed after pooling all sources per language (not the original per-source splits — see Limitations).

Language	Total	Train	Validation	Test
Hausa	23,162	16,213	3,474	3,475
Yorùbá	16,627	11,639	2,494	2,494
Swahili	6,936	4,855	1,040	1,041

Intended use

Training and evaluating sentiment classification models for Hausa, Yorùbá, and Swahili — particularly for benchmarking multilingual co-optimized training approaches (e.g. AutoScientist) against single-source baselines like Davlan/afrisenti-twitter-sentiment-afroxlmr-large.

Limitations

• Label imbalance across sources: NollySenti and the Neurotech Swahili set are binary (positive/negative only). neutral examples come exclusively from AfriSenti, so neutral coverage is thinner relative to positive/negative for all three languages.
• Re-split, not original splits: because splits were recomputed by pooling sources, this is not directly comparable row-for-row to benchmarks trained on the original AfriSenti or NollySenti splits. Use as a fresh baseline, not a drop-in replacement.
• Neurotech Swahili has no official original split — an 80/10/10 split was assigned with a fixed seed (42) before being pooled and re-split here.
• Swahili domain composition differs from Hausa/Yorùbá: it is roughly half Twitter, half social-media/product reviews, while Hausa and Yorùbá are Twitter-dominant with a smaller movie-review slice.

Citation

If you use this combined dataset, please cite all three original sources:

@inproceedings{muhammad2023afrisenti,
  title={AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages},
  author={Muhammad, Shamsuddeen Hassan and others},
  year={2023}
}

@inproceedings{shode2023nollysenti,
  title={NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification},
  author={Shode, Iyanuoluwa and Adelani, David Ifeoluwa and Peng, Jing and Feldman, Anna},
  year={2023}
}

@misc{neurotech2021swahili,
  title={Swahili Sentiment Analysis Dataset},
  author={Neurotech-HQ},
  year={2021},
  howpublished={\\url{https://github.com/Neurotech-HQ/swahili-sentiment-analysis-dataset}}
}

Submission context

Built for the Adaption Labs AutoScientist Challenge (Language category), targeting Hausa, Yorùbá, and Swahili as officially supported languages.