Africa Sentiment Datasets
Collection
The collection contains sentiment-labeled text data for 42 African languages
•
43 items
•
Updated
Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Fulah
Size:
10K - 100K
License:
Dataset Viewer
Fulah
stringlengths 10
480
| sentiment
stringclasses 2
values |
|---|---|
Follow her. pRaateete ye Pratite je.
|
Positive
|
e breed be into mynde of e offrynge of e Lord; e loond;
|
Positive
|
Muccaade dow gole, nder iyaalu men, lamar on.
|
Positive
|
We say doa ga aku , (suteeji no) kaaten ga aku ,
|
Positive
|
That ye wol be good frend unto hem al.'
|
Positive
|
Nder botani, jooni dun wolwata haala ajihon soosai.
|
Positive
|
e dow goonga
|
Positive
|
narrowband: Ngi yiu heh hakka ngin ?
|
Negative
|
Be aald be bad forgot;
|
Negative
|
mo anndaa huunde waawaa jakkoyaade,
|
Positive
|
That ye wol be good frend unto hem al."
|
Positive
|
o gooynaama ley lenyi,
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Negative
|
Jannde habaruuji no jangina nder leydi UK fat.
|
Positive
|
Dooka maako didabol iri go'o e' ko Mendel winndi.
|
Negative
|
Gotta be worth trying, no?
|
Positive
|
Haa fahin,na'uurahoy koy fuu cooya semmbe koy naftortoo.
|
Negative
|
Be mbi: "Jooni a addi ngoonga.
|
Positive
|
Nde laatoto tamre suura duuniyaaru anndaande birnde neebugo hollunde duuniyaaru.
|
Positive
|
Seini bou, nder makroevolushon, gikkuuji muminteeji din fat waawai wartuki nafoodum.
|
Positive
|
Allah tan anndi ndey nyalaade darŋgal laatoto
|
Negative
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Fijirde nden yaawi tokki fijirle ngembitaali jee The Crowd Roars be Winner Take All.
|
Positive
|
taake keno suffer korte holo?
|
Negative
|
Duudal kuugal Ristotle fuuh anddama e Hirna wakkatiire nden.
|
Negative
|
lol He has DEATH! (dum dum dum dum!)
|
Negative
|
Semantics kanjum wani jangugo ma'aana alaamaji - hautirgo alaamaji be haali.
|
Positive
|
Mi wari mi hefti wakkati hautugo fimji petel sali.
|
Negative
|
honto ha fuan dayo (kun ha boku deiino ?)
|
Negative
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kiimiyyaen haa bangeeje feere naftoran shuudi foondo goddi.
|
Negative
|
Nder ko duudi, laayiwol tooke tatabol, mesodam ngol ma , don wurtira diga hakkunde maaje.
|
Negative
|
So wona taw'on kuli e mabbe kulol.
|
Negative
|
Manjum jee Aadaab!
|
Positive
|
O' naftiri bee prime meridian hedi nder Islanji Kanary, ngam kala limngal longitude fuu wonan dow no haandi.
|
Negative
|
Canjiiji di'i nder muminteeji didabe den, to wiyti, foodai dabare deppugo kesi nder muminteeji aranje den.
|
Positive
|
Wakkatiire nde'e boo tabbitinii ummitinki njannde.
|
Positive
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Kaŋko on ngaafi mo, ammaa Allah ummitini mo diga maayde.
|
Negative
|
sithe tiyana dee nokiya hitiya heki weeddaa?
|
Negative
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Benon fu huwobe jodibe nder gure hebai nastugo nder wakili'en majalisa ha didagol kesum dam waddi.
|
Negative
|
Hiisuki muminteeji diya dun hokkata genus don huwa dou ko bernde yidi.
|
Negative
|
Don't be bluuuueeee, we'll be back Monday!
|
Positive
|
Mee hagalee belee hagamehede i di Laangi Sabad?"
|
Negative
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"Saggitooji nayi di Aristotle wallini shardi dow nyamol ""ko wadi"" jaabetee nder laabi nayi ngam wangina yaake kuujeeji nder kiimiyya"
|
Negative
|
De walliri mannginnki ilmu fuungooje ba fannu jannguki.
|
Positive
|
Sainte-Chapelle warti tokkaadum je luttube Chapel ha nder yurope.
|
Positive
|
Hawa ko bura na kaho kyonke wo ALLAH Ta'ala ki rahmat mein se hai, wo rahmat bhi laati hai aur 'azab bhi laati hai.
|
Negative
|
Seini, ko huushal subol ngol wiytiri ta atal nga'al, gikkuuji majjude e' baawo wiytata ummutuki nder tagaade nanndude (ndaaru Dollo's law).
|
Negative
|
Faa hannde on ngalaa hoolaare naa?
|
Negative
|
No laatoo nymngo ngo Caillié yi'i.
|
Negative
|
E inaare nde lilal ngal yottoto.
|
Positive
|
Innde maanudum huundeeji didi warti homonim.
|
Negative
|
Wjaoma eko adi, wonde Lloyd maa windu ciimtugol ngurndam mako fewde Simon e Schuster
|
Positive
|
Manngu lesdi Greenland je hakiikinkeejum fototiree je lesdi republica congo tan
|
Positive
|
haaaa good to be back!
|
Positive
|
Which wan be pirates again?
|
Negative
|
hakkee nden niwre e hewde kulol.
|
Negative
|
Ngam he'ita hakkika duniyaaru , bayanijii majjida.
|
Negative
|
Seini bou, daydaytirki duudde di DNA ndu wala kodwol woodi wattammji wogguki.
|
Negative
|
Dun naftiri e na laabi hosuki gravity-assisted.
|
Negative
|
Narral ngal yerdake ka doggingo poondol nukiliya ngol nder leydi.
|
Positive
|
Style mai ha wakkatii mai wakkatigo andiraama be Opus Francigenum (lit.
|
Negative
|
teda eno God dagalude bidibo bidi tudi ola mayu,
|
Positive
|
Jesús abin sogded: 'We dia e dii be gobsale gannar be dii gobbi gudabaloed.
|
Positive
|
Kiita Allah dow ummaatooje
|
Positive
|
Binndi mbinndaama dow akitekca iga jamanuuru.
|
Positive
|
Alfanu fiscal no darnan to burtago yaasi.
|
Negative
|
Ko en anndi dow malaa'ika'en?
|
Negative
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Gotta be Obama supporters....
|
Negative
|
Yeehova tagi malaa'ika'en hiddeeko o taga lesdi.
|
Negative
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And how sone wolde thys be payde ageyne,
|
Positive
|
Love isn't always on time. dun dun dun duuuun.
|
Positive
|
O warti hautowo fijirde mo watta wakili Pathè (USA) Faransa.
|
Positive
|
Naahande be kootai naftoraago tatal booxiingal.
|
Negative
|
Beijo grande e suuuuper obrigada!;)
|
Positive
|
Nder ilmu fuungooje, kalimaaje nanndude don eini dun don noddade feere-feere.
|
Negative
|
Suura tamre duuniyaaru, dee de "mawde masin" nden cadde bo.
|
Negative
|
Ande her et be tat serve vouz te badast meduwe wan te fest be finending!"
|
Positive
|
Sowuki jokkan limle didi lattidi go'o, keebangal cowuki.
|
Negative
|
Malaa'ika'en ujine sappo ngardi bee maako.
|
Positive
|
Ammaa Allah ummitini mo diga maayde.
|
Positive
|
Dun don nodda wonndaaku juutungal e' simbiyosis.
|
Positive
|
Seini bou, nder huwru zamanuuru, de fuu de waawai yi'eeki ba bandooje.
|
Negative
|
Ha Ingiland ha pendii Window nde duddum be lornii nokkuure mabbe nde huyndee window.
|
Negative
|
Lesdi Jamani hayri wonte aran wiitinki palsapaaku sana'a.
|
Positive
|
Sarde wonnde, aaya daraniika dariinde yo kalimawol go'ootol.
|
Positive
|
Ammaa Muusa doggi diga Firawna.
|
Positive
|
Dabbaji wala baude masin ha besdari, wala ma bana primateji.
|
Negative
|
Bacon yidi datal ngal tuugi dow toggitol taaskitaaki, maaboo foondo.
|
Negative
|
naama eko pi Saakiyo naahosi."
|
Negative
|
Kanngal woni narral mawngal tabbitiniraangal seyda kampita.
|
Positive
|
Dow doo'do kiisaaji wadaama di yaadata be burna goddi kaidaaji kiimiyya be raayuuji.
|
Negative
|
Alfanu moftal-jawdi mun no hautatake be (chanji je hautayi ceede).
|
Negative
|
Dun andiri haala ka wi'I GCD do wangine be andangal Bezout.
|
Negative
|
Moye wonte etnogirapa'on woodi faa'idaaku masin dow ko mo windata dow aadajingam marem lincitoowo fuuh ko mo fanti woodi nafuuda haro ko mo windata
|
Positive
|
Misaaalu, raaru senngo diggol: senngo ngo'o waaway feccee nder reeta, rreta kan feccee reeta, reeta nder reeta kan, nden tokka non.
|
Negative
|
Adan, observation device goto, koko beydi e dubi capande joy, joni wonti measurement tool nafow
|
Positive
|
Protista on tagzon faila hon duudde, ko anndube bayoloji hande jabaayi sosai na ba saalube.
|
Negative
|
yo yurmeende Allah won e o Gorko teddudo teddin neddaagal; Juuldo juudnudo njuulu.
|
Positive
|
Ngam min woni Jawmiraawo, kurgoowo on."
|
Positive
|
o yawtinira ñallal ngal weddagol.
|
Negative
|
Non man faamoto to mi etirgal giravity kimminingal e hiiso saahi mingo hebake.
|
Negative
|
Tawreeta laati nun hakke na?
|
Negative
|
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in Data Studio
Fulah Sentiment Corpus
Dataset Description
This dataset contains sentiment-labeled text data in Fulah 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: 80,686
- Positive sentiment: 45880 (56.9%)
- Negative sentiment: 34806 (43.1%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Fulah
- 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/fulah-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 Fulah
- Cross-lingual sentiment analysis research
- African language NLP model development
Citation
If you use this dataset in your research, please cite:
@dataset{fulah_sentiments_corpus,
title={Fulah Sentiment Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/fulah-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:
3.32 MB
Size of the auto-converted Parquet files:
3.32 MB
Number of rows:
80,686