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README.md

@@ -1,3 +1,101 @@
 ---
+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
 ---
+
+# Combined 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).
+
+## 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`, `dev`, or `test`
+
+## Splits
+
+All three languages use an identical **70 / 15 / 15** train/dev/test split,
+stratified by label, computed after pooling all sources per language
+(NOT the original per-source splits — see Limitations).
+
+| Language | Total | Train | Dev | 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 |
+
+## 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 (e.g.
+  `Davlan/afrisenti-twitter-sentiment-afroxlmr-large`). 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}}
+}
+```