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:
Umbundu
Size:
10K - 100K
License:
Dataset Viewer
Umbundu
stringlengths 7
498
| sentiment
stringclasses 2
values |
|---|---|
U o ñolela, ndaño o fa, haimo o kala vali lomuenyo."
|
Positive
|
Elipi po womwaava vavali a longa ehalo laxe?"
|
Negative
|
yuhapi sni ye, onakipe wanjila
|
Negative
|
Yosiya wa linga osoma yo Yuda eci a kuata anyamo ecelãla.
|
Positive
|
Ali) O ye who believe!
|
Positive
|
Paulu wa popia hati: "Tu yongola oku kala vakuacili kovina viosi."
|
Positive
|
Kuaci Kupas Mola
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Negative
|
Nda wa lia kepako liuti waco, haico o fa.'
|
Negative
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Eli esapulo liwa kolondonge viosi via Yesu vakuekolelo.
|
Positive
|
Alua Baikadamova - Alua Ibrayeva
|
Positive
|
Opo nee Jehova okwa li a tuma Moses namumwaina Aron va ka pule Farao a mangulule Ovaisrael.
|
Positive
|
PaEndjovo daKalunga, ope na ashike omalongelokalunga omaludi avali elipi?
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Negative
|
Mahi etyi ovatumini vetuyeka tukale motyilongo, atulukala ondyuo onene.
|
Positive
|
Oku sumbila Suku oko ono yolondunge viocili.
|
Positive
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Momo lie olonjali viange ka vi ngecelela oku linga ovina viaco.'" - Anne
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Negative
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Songuile vonjila ka yi pui.'
|
Positive
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Onghee hano, okwa li a nyamukula ovadali vaye nombili a ti: "Oshike mwa konga nge?
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Negative
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Momo lie o nukuila oku linga osoma?'
|
Positive
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Jon: Ndi sima okuti ondaka yaco yi tiamisiwila ku Suku.
|
Positive
|
Momo ove wa sovola ovina viosi.
|
Positive
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A Suku, wa ndi longisa tunde vutila wange.
|
Positive
|
Ivaluka Vamanji va Litumbika Kupange Wotembo Yosi
|
Positive
|
Onduko yove yi sumbiwe." - Mat.
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Negative
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Tungelo ve ku tu tuamena,
|
Positive
|
Eci ca kapaile omuenyo wange kohele kuenje olonjanja vimue nda ambataile uta.
|
Negative
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Satana, omupukifi munenenene, okwa kala ta 'twikifa eendunge daavo vehe na eitavelo' oule womido omayovi.
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Negative
|
Ame nda kolelele ku Suku, pole, sia tavele ku Satana." - ROGELIO.
|
Positive
|
Wa lekisa okuti ukuacili kovina vitito.
|
Positive
|
Kape na omalimbililo kutya okwa hovela okuhongwa diva konima eshi a shitwa e li omudalwa wotete waKalunga.
|
Positive
|
Olayeye Olumuyiwa,
|
Positive
|
Onda ehena elela popepi naKalunga eshi nda li monghalo oyo idjuu."
|
Positive
|
Olondaka vi kuãimo vi kasi ndombangulo yimue Ombangi ya Yehova yi pondola oku kuata lomunu umue posongo.
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Negative
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Ile otashi dulika Omukriste a kale ta fininikwa kovapambele ovo vehe fi ovaitaveli a hombole ile a hombolwe 'manga ina kulupa.'
|
Negative
|
aliye vali sooti kheyli dare
|
Positive
|
Ola li la hongaula nokuyula Ovaisrael vahapu, ndele tava efa po okulongela Kalunga kashili.
|
Positive
|
Omo liaco, ka kisikiwa oku tu sapuila esunga liaco.
|
Positive
|
Eteke Lioku Ivaluka Ava va fa.
|
Positive
|
Olye ta vulu oku gu uva ko?"
|
Negative
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Mbela ova li ngoo tava ka dula okuuda ko Omhango yaKalunga ngeenge kave shii elaka lOshiheberi?
|
Negative
|
Omuenyo ka wa lelukile Kakristão vokaliye.
|
Negative
|
Eye o laika oku viala omanu vange."
|
Positive
|
Ngeenge onde va lekele va ye komaumbo va fya ondjala, otava ka pundila mondjila, osheshi vamwe vomuvo ova dja kokule."
|
Negative
|
Oha kala efimbo lihapu pamwe nomumwatate omunamido nokupwilikina kuye nelitulemo eshi ta popi.
|
Positive
|
Ovo, asongui volomeke."
|
Positive
|
Eli efindano linene kovatumwa.
|
Positive
|
Ewan ko! ndi ko ALAM!
|
Positive
|
Ashike kava li ve shii efiku Omwene wavo te uya, onghee ova li va pumbwa okukala oupafi.
|
Positive
|
Nda nda va tuma onjala kolonjo viavo, va ambukila vonjila, momo vamue pokati kavo va tunda kupãla."
|
Negative
|
ale la valalua me hatavivile igogolu ovola,
|
Negative
|
Suku ka tambulula kohutililo ndoyo.
|
Negative
|
Fimbo 'onhalanheni yaKalunga ye va teelela pomafiku enya aNoa,' Kalunga okwa li a ninga po omalongekido opo a xupife Noa noukwaneumbo waye.
|
Positive
|
Kuenje o lombolola eci ci sukiliwa oco omunu a popeliwe poku popia hati: "Likolisili oku iñila vombundi ya sukatela."
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Negative
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sumba waikelo sawah,
|
Negative
|
Vamue pokati 'kakamba' vange, va fetika oku fenya olodroga; vakuavo va liwekapo oku endaenda kosikola.
|
Negative
|
Tu tumbika kokuove onjo,
|
Positive
|
Yesu wo likuminya hati: "O ka kala kumue lame Vocumbo Celau."
|
Positive
|
Pefyo lavo opo hava tokolwa ngeenge ova wana
|
Negative
|
Pole, ivaluka okuti, vamanji vana va amamako loku pandikisa, va ka tambula onima.
|
Positive
|
Ndiaye Abdoulaye Penda
|
Positive
|
Otava pula kutya fiyo onaini tava kala ngaha medu lavo vene.
|
Negative
|
"Ndongise Oku Linga Ocipango Cove"
|
Positive
|
siran ndun ko muliliyota ko Jahan-
|
Negative
|
Omo lionjongole yoku mõla eci eye a kala oku lilongisa, nda fetikavo oku lilongisa.
|
Positive
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Vetiya olonjeveleli viosi oku tala ovideo yosi.)
|
Positive
|
Yesu wa popia hati: "Okuambambe!
|
Negative
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Ovilia viaco vi linga eteku lianyamo epanduvali elambu ana a laika oku iya oco omanu ka va ka fe lonjala.'
|
Negative
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Eci nda pasuka loku vanja omõlaco wa fa, nda limbuka okuti omõlaco hawangeko.'
|
Negative
|
yambuma toku konjulemele,
|
Positive
|
Toke cilo, etambululo Daviti a eca, li lekisa okuti eye kuatele ekolelo lia pama.
|
Positive
|
Omo liaco, ovaprofeto vesanda tu pondola oku va limbukila kalongiso avo kuenda kovilinga viavo.
|
Positive
|
Osha li sha fa ovatondi va 'tinha,' ile va nyeka ko nonyanya omaumbo oshilando.
|
Negative
|
Yehova wa sapuila Mose hati, 'ku ka yokoke.
|
Positive
|
Tala vali kefetikilo liocinimbu eci.
|
Positive
|
Vutuhu ngoco ua vundilile linga cizango ca Njambi ci lingike kati ceni.
|
Negative
|
Aveshe ova li ko pefimbo eshi Jesus a li kombada yedu nova shanga kombinga yaye.
|
Positive
|
Tradução Anytime you like
|
Positive
|
Aakwanegongalo oyali ya holoka momwaalu omunene kelongeloKalunga Osoondaha ndjoka.
|
Negative
|
Ovina nda lilongisa konembele ka via ñuatisile.
|
Negative
|
Nalo yembo mare umbu lupe molkolie ulu te teringimunge Romo yemboma moloringimunge pep te siringi nosiku moloringi.
|
Negative
|
VIUMA viose via cili vi tua kala navio via fuma kuli Njambi.
|
Positive
|
Noke eye wa popia hati: 'Ene wa lisoki lalume vaco.
|
Positive
|
Ulume kumue lonjo yaye yosi va kolela Yesu."
|
Negative
|
Votoka; eye wa ku vilikiya."
|
Positive
|
Ngã pe pandu u'am: 'Jandema'e sawa'e te rape ke emutatambyk 'y.
|
Negative
|
A Kalunga ño, leci cilimo sivayi.
|
Positive
|
Ovo va popia vati: 'Tu litungili olupale kuenda tu kalamo.
|
Positive
|
Okuti katukevelela ovafendeli vae tupu vakala tyaongana?
|
Negative
|
Mana yetu Saimi wa papatisiwavo kunyamo waco.
|
Positive
|
Eye wa popia hati: "A Tate, nda wa panga, njupe okopo eyi.
|
Negative
|
"Eye wa tambulula hati: 'ndi sukila oku enda kilu.
|
Positive
|
Ko kaafir-u chavey, ko momin-u chavey,
|
Positive
|
Nda wa ku yeva, wa kuatisa manjove."
|
Positive
|
Eye o ka "nyõla ava va nyõla ilu lieve."
|
Positive
|
Sokolola ndeci okuti, ekamba limue lio kosikola li ku sapuila hati: "Sitava okuti kuli Suku.
|
Negative
|
Nda ove vu Kristu, tu sapuile ocili."
|
Positive
|
yetu hena wocekiye onkeyapi sni,
|
Positive
|
Onjanja yipi yasulako nda soneha ukanda umue woku eca olopandu?
|
Negative
|
Tala nghee to dulu okuyakula mo omukulukadi woye moilonga yaye.
|
Negative
|
Jesus okwe va lombwela yo kutya ove na okwiilikana opo va pewe oikulya yefiku, opo omatimba avo a dimwe po nosho yo kombinga yoinima ikwao yopaumwene.
|
Negative
|
Koloneke vilo, tu kuete ovina vialua okuti Avirahama ci sule.
|
Positive
|
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in Data Studio
Umbundu Sentiment Corpus
Dataset Description
This dataset contains sentiment-labeled text data in Umbundu 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: 83,350
- Positive sentiment: 48940 (58.7%)
- Negative sentiment: 34410 (41.3%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Umbundu
- 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/umbundu-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 Umbundu
- Cross-lingual sentiment analysis research
- African language NLP model development
Citation
If you use this dataset in your research, please cite:
@dataset{umbundu_sentiments_corpus,
title={Umbundu Sentiment Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/umbundu-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:
3.36 MB
Size of the auto-converted Parquet files:
3.36 MB
Number of rows:
83,350