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
Rundi
stringlengths 5
496
| sentiment
stringclasses 2
values | __index_level_0__
int64 0
373k
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|---|---|---|
Yavuze ati: "Data, nimba ubishaka, igizayo iki gikombe kimveko.
|
Negative
| 0 |
Abantu bimana Zakat, Imana yabateguriye ibihano bihambaye.
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Negative
| 1 |
'Ntimuzi umusi Umukama wanyu azozirako.' - MAT.
|
Negative
| 2 |
Abigishwa bavuga bati: "Indwi, hamwe n'udufi dukeyi."
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Negative
| 3 |
Kandi muri ico gihe abantu bawe bazorokoka, uwo wese azosangwa yanditswe muri ca gitabu."
|
Positive
| 4 |
kubera iyo mpamvu, baba abahakanyi kubera kuvuga bati: abamarayika ni
|
Positive
| 5 |
Araheza wa muhungu abarira se ati: 'Dawe, naracumuye ku Wo mw'ijuru nongera ngucumurako, singikwiye kwitwa umwana wawe.'
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Negative
| 6 |
Nkakurenza amataba,
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Positive
| 7 |
Yezu yariko arasenga, ahanga ijuru ati: "Dawe, ubutigu burageze: ninahaza Umwana wawe, Umwana wawe na we abone kukuninahaza yongere ahe ubuzima budahera abo wamuhaye bose kubera ububasha wamuhaye ku citwa ikiremwa cose.
|
Positive
| 8 |
nabo, kandi barahakanye Imana n'Intumwa
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Negative
| 9 |
Bukeye mu gitondo barahamagarana.
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Negative
| 10 |
Hari aho umwe muri mwebwe yoba atazi kubabarira mugenziwe canke ugasanga iyo ngorane muyifise mwempi.
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Negative
| 11 |
wanyu mwicishije bugufi kandi mu ibanga, kuko Imana idakunda abarengera"
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Positive
| 12 |
No kuba mw'Iyi si y'Imibabaro,
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Positive
| 13 |
Yemwe bantu banje, ababarongora, barabazimiza, kandi bazimanganya inzira mwociyemwo.
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Positive
| 14 |
Ariko ntiyagiraniye ubucuti somambike na Yehova canke ngo agire icipfuzo gikomeye co kumuhimbara.
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Negative
| 15 |
Icigwa rero kiratomoye: Ntukwiye 'gutinya' wiyumvira ko Imana itakubona.
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Positive
| 16 |
Witwararike kubwira inkuru nziza uwuharyama wese imbere y'uko agenda."
|
Positive
| 17 |
Nuce wiha ishusho rero ukuntu abamarayika basemerera bakubwira bati: "Ntiwemere ibinyoma vya Shetani!"
|
Positive
| 18 |
Tuzogirira iki mushiki wacu umusi azosabwa?"
|
Negative
| 19 |
Birahambaye ko umenya ico kintu kubera yuko hari ibintu bikomeye Imana yavuze vyerekeye kazoza ka vuba bitazobura kugira ico bihinduye kuri wewe.
|
Positive
| 20 |
Yaciye ababaza ati: "Kubera iki mwijiriwe mu maso uno musi?"
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Negative
| 21 |
Niwibaze uti: 'Ubwo Imana ntiyoba iriko irabakoresha kugira ngo inyiyegereze?'
|
Negative
| 22 |
Inyifato yo kwigumya no kubika mu nda gushika umusi bizosambuka bikaja ahabona;
|
Positive
| 23 |
None mwebwe ntimuzirusha agaciro?"
|
Negative
| 24 |
Abo bazorekurwa umusi ubutungane butazoba bukiri mukwaha bwabo basuma!
|
Negative
| 25 |
Ku bw'ivyo, ntidukwiye none kumushemeza mu masengesho yacu "incuro indwi ku musi," ni ukuvuga kenshi cane?
|
Negative
| 26 |
Maze atangura kubwira abantu biwe ati: 'Ehe raba!
|
Positive
| 27 |
Abantu boshobora kutubwira bati: "Reka twiryohere.
|
Positive
| 28 |
Kuko ukuboko kwawe kwandemēra umurango n'ijoro."
|
Positive
| 29 |
Jewe ko ndi umukunzi wawe, sindakurutira abana icumi?"
|
Negative
| 30 |
Tukeran sama punyamu ya.
|
Positive
| 31 |
Nta gukeka ko bigishije umuhungu wabo Samusoni itegeko ry'Imana kandi biboneka ko utwigoro twabo tutabaye impfagusa.
|
Positive
| 32 |
Egome, abansi b'umuntu bazoba abo mu rugo rwiwe bwite."
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Negative
| 33 |
Canke uwavyawe n'umugore, vyoshobka bite ko aba umugororotsi?
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Negative
| 34 |
Elize abibonye, arasemerera ati: "Yewe ga dawe we, yewe ga dawe we!
|
Positive
| 35 |
Abantu benshi barasenga mwene abo beranda be n'abamarayika, bizigiye ko babasabira ku Mana.
|
Positive
| 36 |
Ubuzima bwoshobora gusa n'aho ari urukurikirane rw'imisi y'umwiza. - Umus.
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Negative
| 37 |
Uko ivyawe vyoba vyifashe kwose, rimbura akarorero ka Musa.
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Negative
| 38 |
"Ariko si umukinyi wanje, ari muri Manchester.
|
Negative
| 39 |
Eka kafirfiri kadogo kwa hio mutura bro
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Negative
| 40 |
buyosaba obuyosaba buyosaba abuyusaba obungeyusaba bungeyusaba
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Positive
| 41 |
Yezu yaramaranye umwanya n'abayoboke biwe, arabigisha ingene Imana ibona ibintu.
|
Positive
| 42 |
Ati nutakora neza icaha kibunze kurugi kandi niwewe kirondera.
|
Negative
| 43 |
umuntu we abaye iki ngo umwibuke,
|
Positive
| 44 |
Mugabo ubu turemerewe kubwira abandi imihezagiro abasavyi b'Imana bifitiye muri iki gihe.
|
Positive
| 45 |
Abazoja mw'ijuru si bo bonyene bahabwa impera.
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Negative
| 46 |
Ico canditswe kivuga giti: "Imana ubwayo izobana na bo.
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Positive
| 47 |
Nikodemo aramubaza, at'Umuntu ashobora ate kuvyarwa ashaje?
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Negative
| 48 |
Naho manu yari ingabirano iva ku Mana, ntiyashoboye gutanga ubuzima budahera.
|
Negative
| 49 |
Wewe ufise amajambo y'ubuzima budahera."
|
Positive
| 50 |
Ntiwigere wibagira ko yatanze Umwana wiwe ku bwa bose, harimwo n'umwana wawe.
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Positive
| 51 |
Egome, umuntu arashobora kugirira ubushangashirwe Imana mu mutima wiwe.
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Negative
| 52 |
" Ibimenyetso bizoranga abazoba bemeye nivyo vy'ibi: Kw'izina ryanje bazokwirukana amashetani....
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Positive
| 53 |
Dawidi yanditse ati: "Ni nde yoduga ku musozi wa Yehova, kandi ni nde yohagarara ahantu heranda hiwe?"
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Negative
| 54 |
ni we afatanya ibintu vyose, akamenya ibivugwa vyose.
|
Positive
| 55 |
Nowa yaraburiye abantu ku bijanye n'Isegenya ryagira rize, mugabo ntibumvirije.
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Negative
| 56 |
Ko werekanye ko uri umwizigirwa mu kintu gitoyi cane, nugire ububasha ku bisagara cumi.'
|
Positive
| 57 |
Ni koko bazovuga ivy'i Siyoni bati: aba n'aba niho bavukiye.
|
Positive
| 58 |
Ico bemera gusa nuko ngo boba bararashe urusasu rumwe mu kirere mu ntumbero y'ukugabisha abo bariko baraca mu kinyegero.
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Negative
| 59 |
Umugororotsi awirukiramwo agakingirwa."
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Positive
| 60 |
Erizooba oburuuru nibutweerwa omuri Ntungamo kokutorana omujwekyerwa omwishengyero ahabwa ishaza rya Ruhaama.
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Positive
| 61 |
"Turi ibihimba vya bagenzi bacu." - EF.
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Positive
| 62 |
ni Wewe Rudasumbwa ugenza vyose;
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Positive
| 63 |
Umwe amuronka uko ashaka,
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Positive
| 64 |
Benshi rero baramwemera.
|
Positive
| 65 |
Raba, Yohani Batista yaje ata mukate arya, ata mvinyu anywa; none muvuga muti: arimwo ishetani.
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Negative
| 66 |
Shetani ariko ararushiriza kugaba ibitero ku basavyi b'Imana.
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Negative
| 67 |
Yongeyeko ati: "Ukuboko kwawe kurakomeye, ukuryo kwawe gushizwe hejuru."
|
Positive
| 68 |
Ariko rero, nari niyemeje kubaho mpimbara Imana.
|
Positive
| 69 |
Aguma asenga Imana yiwe gatatu ku musi.'
|
Positive
| 70 |
Ariko iyo utavyizeye ,ugwa mu mutego kubera kutumvira.Benshi bizeye ,babiharuweko nk'ubuhizi.
|
Positive
| 71 |
mu gitondo bamera nk'ivyatsi bikura,
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Negative
| 72 |
Raba ivyo abantu bo mu bwami bwo mu buraruko bwo muri Isirayeli ya kera bariko barakora.
|
Positive
| 73 |
None mwe muterekera impiri muyita imandwa mwibazako abamaze kuzira ubusa banganiki?
|
Positive
| 74 |
Yavuze ati: "Twarabuze inzu yacu n'abo mu muryango wacu nka bose."
|
Negative
| 75 |
Bazoyaga ubuninahazwa bw'ingoma yawe, bongere bavuge ubushobozi bwawe, kugira abana b'abantu bamenyeshwe ibikorwa vyiwe vy'inkomezi, be n'ubuninahazwa bw'ubwiza bwakaka bw'ingoma yiwe."
|
Positive
| 76 |
Oyaye; bwarashitse ku ntumbero bwari bufise yo gutanga imburi.
|
Negative
| 77 |
Bompi barateye akagere ubuyobozi bahawe na Se wabo abakunda.
|
Positive
| 78 |
Yozefu arabamenya, ariko bo ntibamumenya.
|
Negative
| 79 |
Mariya ati: 'Ivyo bishoboka bite?
|
Negative
| 80 |
Zihamagara izazo,
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Positive
| 81 |
Woba ushobora kwiha ishusho uriko urungukira ku vyo Imana isezerana?
|
Positive
| 82 |
Umwami Dawidi yanditse ati: "Amakosa yanje yarenze umutwe wanje; nka kurya kw'umutwaro uremereye, arandemereye birenze."
|
Negative
| 83 |
Intumwa zari zamye ziraharira ku bijanye n'umukuru uwo ari we hagati yabo.
|
Negative
| 84 |
Vyaramfasha guhangana n'umusi ukurikira." - Marilyn.
|
Positive
| 85 |
kadranwa to ajabkhera
|
Positive
| 86 |
Icaha nico kibera aba kristo intambamyi ariko benshi ntibabimenye.
|
Negative
| 87 |
Ubwo hariya hantu mwakuye amazi hehe ko nziko nta mazi ahaba wana?"
|
Negative
| 88 |
Ni ukuri, uyu musi urandutira iyindi yose !
|
Positive
| 89 |
kuko ari Wewe musa nizeye.
|
Positive
| 90 |
Dukwiye gucudika n'abakora ivyo Imana igomba.
|
Negative
| 91 |
Nzorangura indagano zanje imbere y'abamwubaha,
|
Negative
| 92 |
Maze, Aburahamu arapfukama, arunama, aratwenga, yibwira mu mutima ati: "None se, umwana yovyarwa n'umutama amaze imyaka ijana?
|
Positive
| 93 |
Umukama Imana yaranzibuye ugutwi, nanje sinagambaraye, canke ngo niyonjorore.
|
Positive
| 94 |
Abuneri na we avuga ati: "Ndahiye ukubaho kwawe mwami, sindabizi namba!"
|
Positive
| 95 |
Ni bande bazozurwa mu nyuma?
|
Negative
| 96 |
Abapfa ari abizigirwa imbere y'uko ya makuba akomeye atangura, ico gihe baba bashizweko ikidodo ca nyuma.
|
Negative
| 97 |
None ar'Imana, ari n'abo bantu bihaye ikibanza c'Imana, ukuri n'ukuhe?
|
Positive
| 98 |
Uwo mupfakazi yari yararonkeje uwo muhanuzi ivyo akeneye yongera aragira ukwizera.
|
Positive
| 99 |
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Rundi Sentiment Corpus
Dataset Description
This dataset contains sentiment-labeled text data in Rundi 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: 372,663
- Positive sentiment: 209740 (56.3%)
- Negative sentiment: 162923 (43.7%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Rundi
- 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/rundi-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 Rundi
- Cross-lingual sentiment analysis research
- African language NLP model development
Citation
If you use this dataset in your research, please cite:
@dataset{rundi_sentiments_corpus,
title={Rundi Sentiment Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/rundi-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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Size of the auto-converted Parquet files:
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Number of rows:
372,663