Africa Sentiment Datasets
Collection
The collection contains sentiment-labeled text data for 42 African languages
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43 items
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Updated
Dataset Viewer
Yoruba
stringlengths 7
481
| sentiment
stringclasses 2
values |
|---|---|
kikase sugita ADORIBU to hikikae ni asu wa konai darou | Negative |
onim o letu i moru, | Positive |
Awon to gba iyanu lodo Olorun nikan o ba enu ilekun wole. | Positive |
Igba yen nko, bawo ni mo ti fi han fun nyin?" | Negative |
Oba t'O we adete mo (Ko s'oba meji af'Oluwa) | Negative |
Olohun lo ni eto pe ki e beru Re, ti enyin ba je onigbagbo ododo). | Positive |
Ati pe ti ko ba si ododo, nigbana ojiji Olorun lo wa mbe. | Negative |
OSIEC Osun Taiwo Olodo | Positive |
ni áinshun ni áinhun ni áinōhun | Negative |
Otile, olodo! | Positive |
if ala yaara nave ahipaad, | Positive |
O ti funni ni idaniloju nkan yii si gbogbo eniyan nipa O ji dide kuro ninu oku. " | Positive |
Oba t'o mo ohun gbogbo, (The all knowing God) Oba t'o mo ohun gbogbo, (il Dio onnisciente) | Positive |
Saanse Jaye Aatak Aatak, | Positive |
Kini a o se nigbati onigbagbo kan ba dede ku. | Positive |
O tun so pe oun ko ti i setan lati ba wa soro, ati pe ti akoko oro ba to, oun yoo ranse si wa. | Positive |
bali to aasiwan | Positive |
ni èmi yóò fi ohun ìyanu hàn án." | Positive |
Ti ko ba je wi pe awon eni ti a ni ko fe ni loju fata senu ni, kilo fe mu oju ti n gun ni tun maa tani bi ata. | Negative |
Eyin ni ohun elo to po,ipo gidi. | Positive |
Olúwa mi àmòimòtán. | Positive |
Ati bakanna, w haven ti t afterlé àw godsn strangel strangerun àjòjì, ki nwon le ma sin won. | Positive |
Wa Yusufeen wa be Ishaqeen wa gay re he mu, Min anbiyaeen | Positive |
O tobi ju oun to tobiju, osi kere ju oun to kere ju. | Positive |
O ko ba ri ohunkohun sugbon òkunkun. | Positive |
Kii tan ninu igba osuun, k'ama fi r'amo Lori, | Negative |
Tani ninu yin lati gbogbo eniyan re? | Negative |
Yi ajinde ko ni waye si aye re. | Positive |
Omó atanna Ifa yoro yoro lé kú lo Ikú ti nbe ni le yi kóderú kojade Owiri wiri a o fi iná Ifa wi wón lára | Negative |
Mo ro, "bayi ni mo oye bi o lati tan Islam ni United States ati Europe." | Positive |
Obesere Oye Olohun (God Knows Best) | Positive |
Iwon't be online tonight! | Negative |
Be tie bu won, ase Olorun ni, Olorun ti fun won ni ore ofe lati pa awon oruko yi re ni. | Positive |
Wipe, ara eniyan o tobi wà. | Positive |
Adegbulu AJ, | Negative |
Gbogbo aye, gbe Jesu ga, Angel', e wole fun E mu ade Oba Re wa, Se l'Oba awon oba. | Positive |
Adebukola Ogunrinde, | Negative |
A si fi obinrin na sinu ile Farao. | Negative |
'It's too late to go there today.' ïåðåâîä íà ðóññêèé ÿçûê è òðàíñêðèïöèÿ | Negative |
Nigba miiran o ma n wo " | Positive |
difa fun Marunlelogojo igi oko, | Positive |
Beena ona imi leleyi la je ko daju.. | Negative |
OS MJI O gbo koran koran Babalawo agbe lo d'ifa fun agbe Eyi ti yoo ma fi oran gbogbo je ho-ho Won ni ko kara nile, ebo ni ko s Mo je, ho-ho ki ngo; ho ho Iworo isope, e wa ba ni lajase ogun Ajasegun laa ba ni lese orisa | Negative |
See n mi sa ojee (Ibadan ijinle). | Positive |
Ibinu oro ti baba naa so ni won ni Iyabo ba pada sile yii, bee lo seleri fun toko-tiyawo naa pe oun setan lati fina si gbogbo dukia won, ati pe ihooho ni won yoo ba jade kuro nibe. | Negative |
Opolongo bi eyo umbo opolongo baba nishe omo niye oniye oun babalawo | Positive |
opolongo bi eyo umbo opolongo baba nishe omo niye oniye oun babalawo | Positive |
Aaaah je suis rassuré ! | Positive |
atijouru. ti uurji to-morrow. | Negative |
"A wa si Allah ati fun u a yio pada." | Positive |
Ni ipo yii, Emi ko ni idunnu. | Positive |
"O, ibojì yìí àti òkúta tí mo ti gb between láàárín èmi àti youyin, | Positive |
Nibi wan sowipe eni to keeko, Ko kin sukun fun eni to wa laaye tabi eni toti ku. (segbe:) E yo wan kuro niwaju. | Positive |
Enia ko ni agbara, | Positive |
Kini idinku fun agbegbe ibi isinmi? | Positive |
Ibeere: "Se o ti ni iye ainipekun?" | Positive |
E ri mi pe mo kere le fi yan mi je, Ifa inu mi ko Kere, | Positive |
Mu dinari kan fun mi wá, kí n lè rí i. " | Negative |
ol lu ni nit, | Positive |
Fun iná ti a ti ràn ninu ibinu mi; o yoo sun lori nyin. " | Positive |
si! lo amo! jeje | Negative |
Ati eniti o ti yipada, ki yoo pada wa? | Negative |
Aw?n ?na ti o ba ?sin Islam mu fun Is? ?l?hun ati Aabo R?By Abdur Rahman Muhammadul Awwal | Negative |
Bo jo ro, iwo ni ma balo, | Positive |
wò ó, kí o sì rí ìtìjú wa. | Negative |
Ama Jarn la wo dayge age gà. | Negative |
Ala ti je topic. | Positive |
Akoko ti Japan nigbagbogbo n yipada. | Positive |
ti ni guyé rañaa que gué'. | Positive |
Bí o l'ówó bí o kò ní 'wà 'nkó, | Negative |
E ku gbogbo igba mi o, Doctor. | Positive |
Ko si esin teeyan n se laye yi ti Olorun ko gbo adura e. | Negative |
Ewebe tutu ati saladi adalu, ale fun meji | Positive |
Hummmm oro po ninu iwe kobo.God knows d best. | Positive |
Nitori eyi, Oluwa le dariji ese wa ati fun ileri iye ainipekun ni paradise. | Positive |
Arabinrin wa fun wa ni okuta marun tabi ... | Positive |
Jot thaki jone jogi nipaayaa, | Positive |
"Kan ik je joinen?" | Negative |
Emi o pe lori Oluwa mi. | Positive |
A pada wa ni gbogbo igba ti a ba wa ni Omaha Area. | Positive |
êàêèå óñëîâèÿ òðóäà ó âàñ íà ðàáîòå; | Positive |
Ki B'Aladura titun, ni ti Baba wa yan, O ti l sd Baba wa Tunlase At'ewe m lrun; Dagba sinu Iml, Eniyan gbagbe sugbn lrun niran. | Positive |
A dupe, a sin a Olorun ti o mo ohun gbogbo. | Positive |
Adia funAkinoro ti se omokunrin onko, | Positive |
to jara sidai aakar jaliye, | Positive |
reveler ni le jour ni Iheure. | Positive |
Ni ojo ti o sunkun pe ohun ni ibudo, | Positive |
Ma jeko ya e lenu ti ba lo ra beamer, Able God lasan legbo te lo n sina | Positive |
Min ba dɛnkɛninya n ma, wo tigi ti to pinpi rɔ. | Negative |
Aasman Chukar Aa Gaye Bhi Agar, | Positive |
Awon eranko gan, o fe ku. pelu agbara lon fi ku pa wan. ìpànìyàn leleyi. | Negative |
"oluo popo eye nile, oluopopo aba obo ekan nile odura oluo popo." | Positive |
Oli, je to ona | Positive |
Ya Miyaji Ki Aarti pratidin, | Positive |
Lamento lo de tú messenger, ojalá lo recuperes. | Negative |
Adogan kekere gbe ko ko nla ru, | Positive |
Ma lo fi yan mi kere, kere | Positive |
By Falope Ibukun | Positive |
Satan is under my feet ko je lo so ewe agbeje mowo. | Negative |
un wuwo bi erin, | Positive |
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Yoruba Sentiment Corpus
Dataset Description
This dataset contains sentiment-labeled text data in Yoruba 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: 180,921
- Positive sentiment: 95181 (52.6%)
- Negative sentiment: 85740 (47.4%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Yoruba
- 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/yoruba-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 Yoruba
- Cross-lingual sentiment analysis research
- African language NLP model development
Citation
If you use this dataset in your research, please cite:
@dataset{yoruba_sentiments_corpus,
title={Yoruba Sentiment Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/yoruba-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:
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Number of rows:
180,921