Datasets:

michsethowusu
/

ganda-sentiments-corpus

Ganda stringlengths 1 498	sentiment stringclasses 2 values	__index_level_0__ int64 0 653k
Bwe tutyo bulijjo abeeyisa obubi tubafuula mikwano gy'a ba nnaabwe bwe bafaanana olw'ebyo bye bakola.	Negative	0
eby'emyaka emingi eby'omukono ogwa ddyo ogw'oyo Ali Waggulu Ennyo."	Positive	1
Ebilekeddwa Katonda (nga oggyeko byayogedde wa ggulu ebitaliimu kukumpanya bantu) bye birungi gye muli bwe muba nga muli bakkiriza.	Positive	2
mukyala wo aliba ng'omuzabbibu ogubala ennyo;	Positive	3
Abaffe ani oyo atandikawo e bitonde ate nga agenda kubizzaawo (oluvanyuma lw'okufa kwabyo) era ani abagabirira (mmwe) okuva waggulu ne ku nsi (ate oluvanyuma lw'ekyo) wasobola okubaawo ekisinzibwa kyonna nga kigattibwa ku Katonda! bagambe nti muleete obujulizi bwa mmwe bwe muba nga muli ba mazima.	Positive	4
So nga obulamu obw'enkomerero bwe businga obulungi era bwe bwo kusigalawo.	Negative	5
Bano munda baabagobyemu okuggyako Mukasa Mbidde ne balooya ba Zaake.	Negative	6
baawulira nga ggwe Mukama oli wakati mu bantu bano; kubanga ggwe	Positive	7
kutusasula wadde kutweebaza.'	Positive	8
Abaffe empera y'okukola obulungi (eyinza okuba ekintu ekirala kyonna) okugyako okusasulwa obulungi.	Positive	9
Abange Mmwe Abakkiriza bwemunaagondera ekibinja mwabo abaaweebwa ekitabo bajja kubazza mu bukafiiri oluvanyuma lwokuba nga mubadde bakkiriza.	Negative	10
abantu ab'ensi n'omukono gwo, kubanga balowooleza mu bya bulamu buno byokka, n'emigabo gyabwe giri mu nsi.	Positive	11
Ngikuuma emisana n'ekiro waleme kubaawo n'omu agikola kabi."	Positive	12
Tunemwendisyasyalola oko bandu ababiri hola abo twabya twanzire?	Negative	13
Yalinga ayitiriza nnyo okusaba Allah mu mwezi gwa dhul-hijjatNalagira oyitiriza mugwo okugamba nti Alhamudulillah, Allahu akibar, n'okutendereza	Positive	14
muliggwaawo ng'abafuzi abalala bonna bwe baggwaawo."	Negative	15
Oba bagamba nti (Muhammad) yagigunjaawo, gamba nti (bwemba nga nze nnagiyiiya) kale muleete essuula yonna egifaanana muyite bonna bemunaaba musobodde, nga oggyeko Katonda bwe muba nga mwogera mazima.	Positive	16
Laba engeri gyebatemerera Katonda obulimba, ekyo kimala bumazi okuba nti kibi ekyolwatu.	Negative	17
Kubanga mu ssaawa emu omusango gwakyo gusaliddwa.'	Negative	18
Ekyokubiri: Kwekukkiriza buli kitendo kyonna Allah kyeyetenda nakyo ye kennyini oba Omubaka we (s.a.w) kyeyamutenda nakyo mubwennyini bwakyo mumbeera egwanidde obujjuvu bwe n'ekitiibwa kye.	Positive	19
nga ndi ng'abo be basse abalinda obulinzi entaana,	Positive	20
(Ate mu kukola ebintu byonna) ekiragiro kyaffe tekiri okugyako kimu nga kiringa lutemya lwa liiso.	Positive	21
Lwaki ggwe wekka ggwe otudde, abantu ne baba nga bayimiridde okukwebungulula, okuva enkya okuzibya obudde.	Negative	22
Mazima obulamu bwe nsi muzannyo na binyumu, naye bwe mukkiriza era nemutya Katonda, ajja kubawa empeera ya mmwe, ate nga tabasabye mmali ya mmwe.	Positive	23
Naye tugenda okulaba enkwata yabalongo entuufu Kona ku musino, nga eno bwozanyikiriza abalongo (enfuli).	Positive	24
(Mu kukola ekyo) baagala kuzikiza kitangaala kya Katonda n'emimwa gyabwe.	Negative	25
Obulamu bwe nsi tebuli okugyako muzannyo na kusanyuka, naye mazima ddala e nyumba e yenkomerero y'esinga obulungi eri abo abatya Katonda, abaffe temutegeera.	Positive	26
Era mazima eggye lyaffe ddala bo be baba abawanguzi.	Positive	27
Olwo nno netumugondeza empewo nga etambula ku lw'ekiragirokye mu ngeri ya mirembe neegenda wonna waaba asazeewo.	Positive	28
Ate abo abalimbisa e bigambo byaffe, e bibonerezo bigenda kubatuukako ol'webyo bye baakola.	Negative	29
"Mmwe abantu bange ababeera mu Sayuuni,	Positive	30
ndyoke nfuuke ng'oyo ali waggulu ennyo."	Positive	31
Olwo nno tulyoke tukulage obubonero bwaffe obunene.	Positive	32
gibeere kuye- n'ezadde lye munnyumba olunaku lw'okuzuukira , naye singa abantu baatondwa	Positive	33
Oyo si ye musana, wabula okutegeeza eby'omusana.	Negative	34
accede okukkiriza, okuganya; (to throne) okulya obwakabaka.	Positive	35
Buli musango gw'owulira ku mutima gwo, manya nti aggya kukusonyiwa singa mubwetowaze omukkiriza okubeera omulokoziiwo."	Positive	36
Era ffe tuba okumpi naye okusinga mmwe, naye temulaba.	Negative	37
Obulamu eri Abatuukirivu	Positive	38
(Kur'ani) yyo teri okugyako okuba nti kya kubuulirira eri ebitonde.	Negative	39
Ate wakatandiika ?	Positive	40
Noolwekyo nze ani okumuzimbira eyeekaalu, okuggyako okumuzimbira ekifo eky'okwoterezangamu obubaane mu maaso ge?	Negative	41
Waakye ooo waakye!	Negative	42
Ewatali Kristu, tugenda kufa era tumalire mu geyeena nga tusasulira ebibi byaffe.	Negative	43
ebyo bye bituuka ku mawanga gonna ageerabira Katonda.	Negative	44
Kyetunaavanga tulema okutya, ensi ne bw'eneekyukanga,	Positive	45
Ye banange emundu gyebabbisa bwekwatibwa efuuke kikwangala bajiteeke kumuliro bagyokye?	Negative	46
oba abaayogera nti, 'Nga katonda wo bw'ali omulamu ggwe Ddaani,'	Positive	47
No ori Meesia ngotulaga-ulaga kalala kugere tumanye!"	Positive	48
Okwenene kuli kuthi ebingyi ebikahebawayo omwa kithunga bikalhua omwa hike-hike ehyo balikyethu bakahayo.	Negative	49
n'abaddu be bonna n'Abamisiri bonna; ne waba okukaaba okunene mu	Positive	50
Bagambe ggwe (Nabbi Muhammad) nti mulaba mutya singa biba bibajjidde e bibonerezo bya Katonda oba olunaku lwe nkomerero nga lwe lubajjidde, (mu mbeera eyo muyinza) okulaajanira atali Katonda omu bwe muba nga mwogera mazima.	Negative	51
Emirembe emeka gye twazikiriza oluberyeberye lwabwe, abaatuuka okukuba ebiwoobe (nga banoonya okutaasibwa), naye nga tewakyali buwonero.	Negative	52
Oba bagamba nti (Kur'ani eno Muhammad) yagigunjawo bagambe nti kale muleeteeyo essuula kkumi enjiiye ezifaanana (Kur'ani) era ng'oggyeko Katonda muyite yenna gwe musobodde (abayambeko) bwe muba nga mwogera mazima.	Positive	53
ebikyaamu by'akola, kyekimu abeera mukulembeze oba muggule ne mukamawo musonyiyi ekimala era musaasizi	Positive	54
Netubawonya n'abantu baabwe bombi akabenje ak'amaanyi.	Positive	55
noolwekyo ayigiriza aboonoonyi ekkubo lye.	Positive	56
Mazima nze nnasanze nga omukyala y'abafuga ate nga yaweebwa buli kintu, era alina namulondo empitirivu.	Positive	57
olunaatuukanga ekibuga ekisinga okuba okumpi omuntu eyattibwa,	Positive	58
" Oyo yenna akyusa eddiini ye (n'ava mubusiraamu) mu mutte nga."	Negative	59
Oyo yenna e birizitowa e bipimwa bye, abo nno be bagenda okubalibwa nti batuuse ku buwanguzi.	Negative	60
Anan ye oteebe ole, "Mi ano Kamuktaindet ne po kirwoogetap imanda?"	Negative	61
Era butto akamuddwaamu ayamba bino;	Positive	62
Kiki ekikukozezza obubi omuddu wo? era kiki ekindobedde okulaba ekisa	Negative	63
Assalomu Alaykum duslar kanalimizga obuna buling,	Positive	64
"Kyendiva mbalumba ng'empologoma, era ndibateegera ku kkubo ng'engo.	Positive	65
Olunaku lw'alijjukira omuntu ebyo byonna bye yakola.	Positive	66
Yagattako nti: "Mujja kuba bajulirwa b'ebintu ebyo." - Luk.	Positive	67
binene, byazimbibwako ebigo okutuuka mu ggulu; era twalabayo abaana	Positive	68
bino byonso byotwamusambila'bi."	Positive	69
Ebigambo byaffe bwe bibasomerwa mu bunnyonnyofu (bwabyo), ekyekwaso kyabwe tekiba okugyako bagamba (bweba nga eriyo okuzuukira kale) mukomyewo bakadde baffe (abaafa) mwemuba mwogera mazima.	Negative	70
ne kiba kibi gy'oli.	Negative	71
kubanga Mukama yafuula Yoludaani ensalo wakati mu ffe nammwe,	Positive	72
Oba olufuufu lwokka okukilabirira obulungi; singa wabaawo awavudde akadongo kiddabirize.	Negative	73
Bakweralaasizaako nti baasiramuka, bagambe nti temundalaasizaako busiraamu bwa mmwe, wabula Katonda abenyumiririzaako okuba nti yabalungamya eri obukkiriza bwe muba nga muli ba mazima.	Positive	74
Singa twabassiza ba Malayika , era abafu ne boogeranabo, ne tubakunganyiza buli kintu ne kibaawo, tebandikkirizza okugyako Katonda nga ayagadde wabula mazima ddala abasinga obungi mu bo tebategeera.	Negative	75
ekitonde ekinaabeera nga kisobola okuzimba ensi eno, mpaka ku lunaku lw'okuyimirira mumaasoge n'okusasula	Positive	76
Era mmwe temuli bayinza kulemesa (Katonda) ku nsi ate era nga ogyeko Katonda temulina mukuumi wadde omutaasa.	Negative	77
Nabbi (s.a.w) yalaganyisa ekibonerezo ekinene eri abo abakola ekikolwa kino nagamba nti:	Negative	78
Musa naagamba nti, ayi Mukama omulabirizi wange, nsonyiwa ne muganda wange, era otuyingize mu kusaasirakwo, anti bulijjo ggwe musaasizi asinga abasaasizi bonna.	Positive	79
Era abange mmwe bantu bange, mazima nze mbatiisa olunaku lw'enduulu.	Negative	80
Ogenda noolaba amaato nga gakola ekkubo muyo, olwo nno musobole okugenda okunoonya ebigabwabye (Katonda), era kibasobozese okwebaza.	Positive	81
jjubiri gye muli; era munaakomangawo buli muntu mu butaka bwe, era	Positive	82
Olituukiriza ddi omusango ku abo abanjigganya?	Negative	83
Aww man, bye dude.	Positive	84
Abantu tebafuuka bamalayika bwebafa.	Positive	85
Nga bazukkulu, abamu bava mu bannabwe, bulijjo Katonda awulira mumanyi nnyo.	Positive	86
Naye mazima abananfusi tebategeera.	Negative	87
Nga mbatusaako obubaka bwa Mukama omulabirizi wange, era nga mbabuulirira, era nga mmanyi okuva ewa Katonda ebyo bye mutamanyi.	Positive	88
bwe tutyo tunaawaayo ebibala by'akamwa kaffe, ng'ebiweebwayo eby'ente ennume.	Positive	89
Yenna asalawo okuwangaala n'omushiriku, nasula wamu naye aba alinga ye	Positive	90
Singa Mukama omulabiriziwo yayagala abantu abali mu nsi bandikkirizza bonna, kaakati ggwe oyagala kukaka bantu bonna babeere bakkiriza.	Negative	91
Totya, kubanga Katonda awulidde eddoboozi ly'omulenzi w'ali.	Positive	92
Omuntu yenna akola ekirungi nga asuubira empeera okuva eri Allah, ajja kugisanga kulunaku lwenkomerero mu maasa ga Allah.	Positive	93
Labbaani n'ayogera nti Entuumu eno ye mujulirwa eri nze naawe leero.	Positive	94
Endagaano ya Katonda temugitundanga omuwendo omutono, mazima ebiri ewa Katonda bye birungi gye muli, bwe muba nga mumanyi.	Negative	95
Wetegereze okusasula kwa Katonda ku lw'ekibi.	Negative	96
Osinga jjajjaffe Ibulayimu eyafa? . . .	Positive	97
'Baani abanabeera abayambi bange eri Allah?'	Negative	98
ly'abantu mu byonna bye bakugamba: kubanga tebakugaanyi ggwe, naye	Negative	99

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Ganda Sentiment Corpus

Dataset Description

This dataset contains sentiment-labeled text data in Ganda 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: 652,937
Positive sentiment: 381307 (58.4%)
Negative sentiment: 271630 (41.6%)

Dataset Structure

Data Fields

Text Column: Contains the original text in Ganda
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/ganda-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 Ganda
Cross-lingual sentiment analysis research
African language NLP model development

Citation

If you use this dataset in your research, please cite:

@dataset{ganda_sentiments_corpus,
  title={Ganda Sentiment Corpus},
  author={Mich-Seth Owusu},
  year={2025},
  url={https://huggingface.co/datasets/michsethowusu/ganda-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

Downloads last month: 0

Size of downloaded dataset files:

34.9 MB

Size of the auto-converted Parquet files:

34.9 MB

Number of rows:

652,937

Collection including michsethowusu/ganda-sentiments-corpus

Africa Sentiment Datasets

Collection

The collection contains sentiment-labeled text data for 42 African languages • 43 items • Updated 2 days ago