Africa Sentiment Datasets
Collection
The collection contains sentiment-labeled text data for 42 African languages
•
43 items
•
Updated
Mossi
stringlengths 10
271
| sentiment
stringclasses 2
values |
|---|---|
Bãwã idan yã yi salla?
|
Negative
|
"Da zoe rabeem ye, bala, mam bee ne foom.
|
Positive
|
GhaM Ko BadnaaM Kar Gaye Aansu,
|
Positive
|
Nees nkaum yim Dudek ok -
|
Negative
|
Yef Y ne deye, thou gost to helle;
|
Positive
|
Dil me lagi aag na bujh payegi,
|
Negative
|
Lamhon ki ek kitaab hai ye zindagi,
|
Positive
|
Humko kisi kitaab me ye to nhn mila,
|
Positive
|
kau tu yg noob,dah kne delete la kafir
|
Positive
|
qu'il ne false ne ne ploie,
|
Positive
|
mercianþii încearcã sã ne facã sã credem cã ei,
|
Positive
|
tab jaa kar hmne hai ye aajadi paayi ,
|
Positive
|
Ne prefãceam cã afarã e soare,
|
Positive
|
Which ye shall taste before ye hang, to mortifie ye;
|
Positive
|
ba, to, bã zã ku aikata ba, sabõda
|
Negative
|
Mai bine sã taci decât sã vorbesti rãu.
|
Positive
|
Me ya'o samebõrõta
|
Negative
|
Lehrã bin ãyã Maiyã khosiyã lotãdiyã
|
Positive
|
ye rooz too jahanam hamdigaro mibinim
|
Positive
|
Les fards à yeux.
|
Positive
|
Ke Adakunom se mak be yir, aky ti daam yir ke a ti ra yir ?
|
Negative
|
Dindimmaa ko a faamaa ye ko, 'M faamaa, n na keetaafengo dii n na, meng mu n niyo ti.'
|
Positive
|
Bãmb maana fãa, baa a ye ka pa ye."
|
Positive
|
Shyad Meri Kasoor ye hai ki aansu me Pani kam hai,
|
Negative
|
Siala bee ne yãmb paama barka n yiid paga fãa,
|
Positive
|
Ye shaairi ye kitaabein ye aayatein dil ki,
|
Positive
|
Bãmb na n tika bãad-rãmb ne b nusi, la b na n paama maagre.
|
Positive
|
Says, Wae be to ye, waefu water,
|
Positive
|
sã stea munţii fãrã grai,
|
Negative
|
Kam ne bar ar ajã Xitumã karõn kum,
|
Positive
|
ye kaam tum karne waali ho, right...???
|
Negative
|
Moses Ok Nkakra B Y Yie
|
Positive
|
moses ok nkakra b y yie
|
Positive
|
"ai vã mai miraþi cã marea nu vã lasã sã mergeþi!
|
Positive
|
ye jo muhabbat hai, ye unakaa hai kaam -
|
Positive
|
Ba ije markam dja ba me'ã ar amã karõ.
|
Negative
|
Vreau sã mã otie tot satul.
|
Positive
|
La b leoka a Zezi n yeele: Zu-soaba, wa n ges-y-yã.
|
Positive
|
Da ges-y y mens wa yam dãmb ye.
|
Negative
|
Pe ba le yãŋ cã ma yee Yãhã kai- sroŋ, ma ka Nsãn n ga pe tãã yi loho wo.
|
Positive
|
La Israɛll nebã wa n yeela a Moiiz yaa: 'Tõnd kongame.
|
Negative
|
aadmi ne aaine ko hairt me daal diya hai;
|
Negative
|
Mose maa yevese yem ken tar u Midian.
|
Negative
|
Dayã garibi bandagi; samatã shêla karãra,
|
Positive
|
Vã rog sã mã înþelegeþi.
|
Positive
|
T'a Balaam leok yaa: 'Fo yaanda maam.
|
Negative
|
Sã mai cearã o datã.
|
Positive
|
Kon Suhãgan Divã Bãliã Meri Maiyã
|
Positive
|
Maane na maane koyee duniya yeh sari ,
|
Positive
|
ye eh?ape yg d usha tu?
|
Negative
|
kyaa error aa raha hai ye bataye. to mai kuchh bata sakata hu.
|
Positive
|
Bãmb b naasa fãa yaa toor-toore."
|
Positive
|
All Die be Die nti, yes nso yentie obiaa dabi ara da,
|
Positive
|
La bõe la d ne a Miise wakatã?
|
Negative
|
choke dee na toog toog kon.
|
Negative
|
N-o sã mã crezi,
|
Positive
|
Bala tõnd yãa bãmb ãdg yaanga, la tõnd waame n na n waoog bãmba.
|
Positive
|
Sabõda makãho yã je masa.
|
Negative
|
hai tujhse ye ummed us maa ki,
|
Positive
|
Khwaab bun ye zara,
|
Negative
|
En kaer Alba neb a vije,
|
Negative
|
Comme la machalï kã supã enfumée,
|
Negative
|
Pm sent to ye both.
|
Positive
|
Pagal hai saala ye,
|
Positive
|
ye samaa, samaa hai ye pyaar kaa -
|
Positive
|
Raam ka naam badnaam na karo,
|
Positive
|
A Zã-Mark ra bee ne bãmb n sõngd-ba.
|
Positive
|
Withã coffeeã whenã youã haveã timeã toã spare.
|
Positive
|
Gom-kãens yɛɛsa bãmba, la b basa a Zezi n looge.
|
Negative
|
Bahu ye to bta tuje ye sab aakhir kisne btaya ha,
|
Negative
|
ã DAN GE R!
|
Positive
|
Haha, blog yaa later.
|
Negative
|
Tõnd sõngda taaba,
|
Positive
|
yesterday y maana.
|
Negative
|
Rehne de ye kitaab tere kaam ki nahi,
|
Positive
|
nikakõixõ a mã ipaoni keskara anã mã ãfe tari kexa ramãkani.
|
Positive
|
Agar ye haal hai dil kaa to koi samjhaaye,
|
Negative
|
A Dieu, dans ma prière,
|
Positive
|
meng zhong de eji uudam -
|
Positive
|
Waseem yaar ye free he to hay..
|
Negative
|
Koi kya de raaye hamaare baare mai,
|
Negative
|
Bɛɛbã na n digame,
|
Negative
|
Vã rog sã mã iertaþi.
|
Positive
|
ye kyaa kar Daalaa tuune dil teraa ho gayaa -
|
Negative
|
ye to duniya ka dastur h yaaro,
|
Positive
|
tiIe oa yTo) uI B oT - oy opy ye BI-
|
Negative
|
Dil ko hai teri justujoo, yaa nabi,
|
Positive
|
M baaba maana kaalem zabrã poor bilfu.
|
Negative
|
ii se loo dare a mii mamaa y a mii tia..
|
Positive
|
Acum o sã ne fie greu, o sã ne parã cã nu mai aveti solutii.
|
Negative
|
Kam che kha v ta ye maa la be dua raa,
|
Positive
|
Ka ce, "Shin, to, bã zã ku yi tunãni ba?"
|
Negative
|
Wo ek boond ka Paani hu Mai,
|
Negative
|
Haaye mere paas se hoke,
|
Positive
|
Lot Ao ye mere DIL ki sada hai,,
|
Positive
|
mam tõe gesã ball rar a ye zãng tele wã zugu.
|
Positive
|
Ore ha'e ko tetã ãngapu,
|
Positive
|
B est pour balle be be balle
|
Negative
|
Rasem a naas-n-soabã, bõe la a maan-yã?
|
Negative
|
Dãri Kluãng, sempãt lãgi terbãbãs ke Tãngkãk.
|
Negative
|
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Mossi Sentiment Corpus
Dataset Description
This dataset contains sentiment-labeled text data in Mossi 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: 125,695
- Positive sentiment: 74409 (59.2%)
- Negative sentiment: 51286 (40.8%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Mossi
- 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/mossi-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 Mossi
- Cross-lingual sentiment analysis research
- African language NLP model development
Citation
If you use this dataset in your research, please cite:
@dataset{mossi_sentiments_corpus,
title={Mossi Sentiment Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/mossi-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
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Size of downloaded dataset files:
3.68 MB
Size of the auto-converted Parquet files:
3.68 MB
Number of rows:
125,695