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