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Mossi
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sentiment
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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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