Datasets:

michsethowusu
/

bambara-sentiments-corpus

Bambara stringlengths 5 494	sentiment stringclasses 2 values
U bè u tulow majò ka mènni kè, nka u tè foyi faamu.	Negative
Nga min hakili cɛnna,	Negative
ma Yɛnŋɛlɛ yɔn sɛnrɛ yofɛnnɛ tɛgɛ.	Positive
yaw kou seww kou doyy waar nga !	Negative
tuze banake maut ka Ek niwala,	Positive
Aw bɛ a furakɛ ni mebɛndazɔli (mébendazole) ye.	Positive
barakallahu laka wa baraka 'alayka wa jama'a kuma fii khayr...	Positive
U bè min kè, u ma o dòn."	Negative
yg lain tu konon nye nk kne wt fyp la..	Negative
Nyamenle kulo kɛ ɔyɛ ye bieko.	Negative
Din ko yeh rakh rakhao wali shakal,	Negative
a ben'a ka jamana n'a ka jama ka hakɛ yafa.	Positive
Jòni bè se ka jò?"	Negative
koun ma ramdan ngoulou yak ma ssi zemzami kan dareb chi kwiyssat !	Negative
Aw bɛ ji in to a wuliminɛn kɔnɔ ka aw ka baarakɛ a la.	Positive
a yé est! ça bug!	Negative
Pine laka kola ilöthikeu la itre iwaane sinöe, haawe, ame la kola traqa la itre wene ka tru, ke, cile catre kö itre ej	Positive
Diyen ye sogomada caman ye (Life has many mornings)	Positive
Tulo bè aw fè, aw tè mènni kè wa?	Negative
A ya a fɔ anu ye ko, "A ya bore do sosa n yɛ la.	Positive
Ahlan wa sahlan fikoun !	Negative
"Bɛdie bɛ mgbanyima ne mɔ na bɛli bɛ mɛla zo.	Positive
Ne b'i deli i ka u dèmè u ka taama ko la ni dèmèni ye min bè bèn Ala sago ma.	Positive
kasi: tenpo lili pini la mi tawa e ma kasi. ma ni li pona mute. kalama	Positive
Merci frerot.Allah yé anw to niongon yé.	Negative
Na fisa adamadie bɛlɛxɛe bɛ."	Negative
wati siapa wati?	Negative
jama ka sunat tareka,	Positive
Yala jigiya bɛ mɔgɔ salenw ye wa?	Negative
Ne bɛ n'kelen na, ani ne sirannen don, u bɛna ne faga.'	Negative
U bè taama u yèrèw negew la.	Positive
Yème bien kurir ene mountanyeu, yé mé san comme ché moi.	Negative
Kɛ ɔkɛyɛ na yɛ ewule kenle ayɛ kpalɛ adɛla yɛ awolɛkenle ɛ?	Positive
Yesu bɛ ka mun de kɛ sisan?	Negative
O don na mɔgɔ caman nana a yɔrɔ la.	Positive
Anyelielɛ a le menli mɔɔ bɛva bɛ ɛtane bɛhyɛ bɛ la	Positive
Li vraî godin qui rûwe bin sins fé mau.	Negative
Ko n'bé sé nafa soro' la	Positive
Djôn yé djɔnkɛɛ lanamɔɔ hakilima di?	Negative
Ka Annabi Yisifu mabihi ti kana kpe o sani, ka o baŋ ba, ka bɛ nyɛla ban bi baŋ o.	Negative
ne fo yɛgɛ ilaa kamaasɛ i' bara imɔ-nyoro gɛsɛ sa mɔ."	Negative
Yo no sé ma yé vé révénirr et yé té racontéré, cé promis.	Positive
Kayes doudou denw ye, hakili djakabo ke, a wati kan.	Positive
Achiika Alizin' shintana maa mi ni bɛ yɛn ti zaŋ ba mi na (n-ti kari ba di saliya).	Positive
Kɔngɔ bɛ jɔn na yan ?	Negative
Mais borom clinique bi nan la mané accepté lolou? wala mom nanou toudé dom ji??	Negative
Dugukolo fana ni a kan fɛnw bɛɛ na jeni ka u ban.	Negative
Mɔgɔ si ma se ka don Alabatosoba kɔnɔ, sani o tɔɔrɔ koba woloɛula ka kɛ ka dafa, olu minnu tun bɛ o mɛlɛkɛ woloɛula bolo.	Negative
Saa bɛka kɛ yɛ nee bɛ ɛraho a, yɛbahɔ.'	Positive
San wɛrɛ, n bɛna angilɛkan karan.	Positive
ser- fen don koun fè, savoir	Positive
Baara caman bɛ ne bolo ka di i ma walisa i k'a kɛ!'	Positive
O bɛ a tigi to ni na fo aw ka dɛmɛ sɔrɔ.	Negative
O dɔrɔn tɛ, a kununna ka bɔ suw cɛma.	Negative
Thei say, "We sall ye sew certayn,	Positive
I baba mi fa a mirima e ma, koni a kɔntɔfilixi ɛ tan nan ma fe ra.	Positive
aw est employé partout. lammakan. lakanna.	Positive
baaraden b'a ka sara kɔnɔna cogo min na,	Negative
té, nin sôïn do. - Mlè-si do, sa wala nyiné ? - A fo,	Negative
Yala i b'a fɛ k'a dɔn fɛn min kɛra o kɔfɛ wa?	Positive
U bɛ wili joona sɔgɔmada fɛ, ka tile bɛɛ kɛ baara la tilefunteni kɔrɔ, tuma dɔw la ali sɛgɛnnafiyɛnbɔ t'a la.	Negative
Ko Farafinna ye kelen ye,	Negative
U ye fiyentò wele k'a fò a ye ko: "I hakili sigi.	Positive
Ndeye Ndiaye yaw boula Adama Fall khassé amnga loko yémalé.	Positive
Den tɛ su bɛ kɛ sunɔgɔ la ka sɔrɔ a ma sin min.	Positive
A senw bɛ i ko samasen tasumamaw.	Positive
O yé yaïe, every night	Negative
merci beaucoup ma douce kala kila	Negative
A' ye kè kiraya nilifèn fè ka tèmèn tòw kan.	Positive
Allah kana sima dron o déyé douwawou yé!	Negative
seronok laa jenjalan kali ni , ye dak ?	Negative
walima ka mɔgɔ dɔ niyɔrɔ minɛ,	Negative
Adenle boni azo a yɛbahola yɛala atiakunlukpalɛyɛlɛ ali yɛahile Nyamenle azonvolɛ ɛnɛ a?	Positive
A sigira sigilan kan, dugu sanfɛ yɔrɔ la,	Positive
Duzu gyima a Nyamenle kile kɛ ye menli ɛyɛ ɛnɛ, na nwane mɔ a ɛlɛyɛ a?	Negative
a ka sigarɛti kelen firi n ma.	Negative
U ye fiyentɔ wele k'a fɔ a ye ko: "I hakili sigi.	Positive
Makari bɛ se sɔrɔ kiri kan.	Positive
Ko: Ni I bolongoni denkelen sin ne mogola, ni o be otigi ke fen ye, i te a bolongoni den tan be sin i yerela folo wa.	Negative
Ni bonjour, ni merci, ni titre correct, ni explications.	Positive
ni bɔnɛ bɔra yɔrɔjan ka na,	Negative
Allah nièssiraniè doron dé bé anw na kari	Negative
A' ye wuli, an ka taa ka bɔ yan.	Negative
Sariya ni kiraw bɛ a seereya kɛ.	Positive
A senw bè i ko samasen tasumamaw.	Positive
hé bé.. ola ou té ka séré frè aw la sa lool	Negative
Ni kunu ma to mogo mi kono, bi bé to i kono	Negative
I ka fɔlen ye 'cyɛn ye.	Negative
nafolo ni kunkɔrɔta fana b'a numanbolo fɛ.	Positive
Ika miy ina wol gninila	Negative
Nin ye julakan baroda ye.	Negative
Aw bɛ a caman fiyɛ ni ninakili fereke ka jugu	Negative
O waati, a' bɛ kɔkɔ bila.	Negative
O bèè n'a ta, sira min ka fisa ni o bèè ye, ne bèna o jira aw la sisan.	Positive
Kɛ Zoova i sran mun bé bó jasin fɛ'n,	Negative
Hai koun sa ye jahan aaj jaha	Negative
But, since ye maun hae tunefu' fame,	Positive
Boro kulu kaŋ maa ni baaru ga kobi ni sabbay se, Zama may boŋ no ni laalayaŋo mana gana han kulu?	Negative
Ne muso bɛ so kɔnɔ.	Positive
karîminn, yâ arhama min kulli rahîminn, yâ a'lama min kulli	Negative

End of preview. Expand in Data Studio

Bambara Sentiment Corpus

Dataset Description

This dataset contains sentiment-labeled text data in Bambara 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: 53,472
Positive sentiment: 29407 (55.0%)
Negative sentiment: 24065 (45.0%)

Dataset Structure

Data Fields

Text Column: Contains the original text in Bambara
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/bambara-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 Bambara
Cross-lingual sentiment analysis research
African language NLP model development

Citation

If you use this dataset in your research, please cite:

@dataset{bambara_sentiments_corpus,
  title={Bambara Sentiment Corpus},
  author={Mich-Seth Owusu},
  year={2025},
  url={https://huggingface.co/datasets/michsethowusu/bambara-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

Downloads last month: 0

Size of downloaded dataset files:

2.55 MB

Size of the auto-converted Parquet files:

2.55 MB

Number of rows:

53,472

Collection including michsethowusu/bambara-sentiments-corpus

Africa Sentiment Datasets

Collection

The collection contains sentiment-labeled text data for 42 African languages • 43 items • Updated 2 days ago