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sentiment
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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
Negative
Nda wa lia kepako liuti waco, haico o fa.'
Negative
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?
Negative
Mahi etyi ovatumini vetuyeka tukale motyilongo, atulukala ondyuo onene.
Positive
Oku sumbila Suku oko ono yolondunge viocili.
Positive
Momo lie olonjali viange ka vi ngecelela oku linga ovina viaco.'" - Anne
Negative
Songuile vonjila ka yi pui.'
Positive
Onghee hano, okwa li a nyamukula ovadali vaye nombili a ti: "Oshike mwa konga nge?
Negative
Momo lie o nukuila oku linga osoma?'
Positive
Jon: Ndi sima okuti ondaka yaco yi tiamisiwila ku Suku.
Positive
Momo ove wa sovola ovina viosi.
Positive
A Suku, wa ndi longisa tunde vutila wange.
Positive
Ivaluka Vamanji va Litumbika Kupange Wotembo Yosi
Positive
Onduko yove yi sumbiwe." - Mat.
Negative
Tungelo ve ku tu tuamena,
Positive
Eci ca kapaile omuenyo wange kohele kuenje olonjanja vimue nda ambataile uta.
Negative
Satana, omupukifi munenenene, okwa kala ta 'twikifa eendunge daavo vehe na eitavelo' oule womido omayovi.
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.
Negative
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
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."
Negative
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
Vetiya olonjeveleli viosi oku tala ovideo yosi.)
Positive
Yesu wa popia hati: "Okuambambe!
Negative
Ovilia viaco vi linga eteku lianyamo epanduvali elambu ana a laika oku iya oco omanu ka va ka fe lonjala.'
Negative
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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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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