README.md

---
language:
- ha
- yo
- sw
license: cc-by-4.0
task_categories:
- text-classification
tags:
- sentiment-analysis
- african-languages
- hausa
- yoruba
- swahili
- autoscientist-challenge
size_categories:
- 10K<n<100K
---

# 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](https://adaptionlabs.ai/blog/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](https://github.com/afrisenti-semeval/afrisent-semeval-2023) | Twitter | Hausa, Yorùbá, Swahili | 40,290 |
| [NollySenti](https://github.com/IyanuSh/NollySenti) | Nollywood movie reviews (human-translated) | Hausa, Yorùbá | 2,510 |
| [Neurotech-HQ Swahili](https://github.com/Neurotech-HQ/swahili-sentiment-analysis-dataset) | 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.