Datasets:
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 | 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 |
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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