Datasets:

michsethowusu
/

pedi-sentiments-corpus

Pedi stringlengths 9 498	sentiment stringclasses 2 values	__index_level_0__ int64 0 423k
Bjale ke ka lebaka lang o bolela le nna ka mokgwa wo?"	Negative	0
Ke bokgopo bja lena bjo bo le hloletšego ditlaišego tše, bjo bo le tsetsemeditšego pelo.	Negative	1
Medimo e tla timelela sa ruri, gomme gwa godišwa Morena fela tšatši leo.	Positive	2
Be ye God fearing, O servants of God!	Negative	3
Morero wa Modimo ka lefase le ka batho go tloga mathomong e be e le ofe?	Negative	4
Empa ge ba betha bahlanka, ba tagwa, kotlo ya bona e tla ba ye kgolo.	Negative	5
Ka morago ga moo bahlanka ba Farao ba re go yena: "Monna yo e tla ba molaba go rena go fihlela neng?	Negative	6
Eupša go na le ba bangwe ba lena bao ba sa dumelego."	Negative	7
Nathane a re: Gona Morena o go lebalela sebe sa gago; o ka se ke wa hwa.	Positive	8
Modimo O nale phomolo feela ho badumedi.	Negative	9
Ka gobane o lahlile batho ba gago, e lego ba ntlo ya Jakobo.	Negative	10
wa bona ka moo bakgopo ba otlwago ka gona,	Positive	11
Letšatši leo le ile la fetola Mukhabudi.	Positive	12
goba yona toka ya gago nageng ya ba ba lebetšwego?	Negative	13
Morwagwe a re go yena: 'Tate, ke fošeditše Modimo le wena.	Negative	14
Ke moka morongwa yo a tšwago legodimong a iponagatša go yena gomme a mo matlafatša.	Positive	15
Retologelang go Morena a sa le file sebaka; mo rapeleng a sa le kgauswi.	Positive	16
Ke ka baka la eng le sa ka la boifa go mmolela gampe, yena Moshe mohlanka wa ka?	Positive	17
O ikapešitše ka leru, gore thapelo e se ke ya fihla go wena.	Negative	18
Yena a mphetola a re: "Tšeo ke diphefo tše nne tša legodimo di a tloga ge di seno rongwa ke Morena wa lefase lohle."	Positive	19
Mangwalo a Makgethwa a boletše e sa le pele gore Modimo o tla lokafatša baditšhaba ka tumelo.	Positive	20
Na o utolletšwe dikgoro tša lehu, Goba na o ka bona dikgoro tša moriti wa lehu?	Negative	21
Re tla ya nago go batho ba geno."	Positive	22
Daga se yãã wee go wara lay.	Positive	23
Botša rena bahlanka ba gago toro yeo, rena re tla e hlatholla."	Positive	24
Botša rena, bahlanka ba gago, toro ya gago, gomme re tla go hlathollela yona."	Positive	25
Ona motse o lokelwa ke kotlo,	Negative	26
ke go thušitše ka letšatši la go go phološa."	Positive	27
Ke moka a re: "Ba ke batlotšwa ba babedi bao ba emego kgauswi le Morena wa lefase ka moka."	Positive	28
Ge a re'alo Jehofa a re go yena: "Ke mang a neilego motho molomo goba yo a dirago gore go be le dimumu goba difoa goba ba go bona gabotse goba difofu?	Negative	29
Lena setšhaba sa ka, baetapele ba lena ba a le timetša, ba le rarakanyetša ditsela.	Positive	30
Lega go le bjalo, gwa tsoga le ngangišano e šoro go bona mabapi le gore ke ofe yo a bego a bonala e le yo mogolo go bona ka moka.	Positive	31
Na rejeeni kwa Mola wenu...	Positive	32
Ka gona mpotšeng toro le tlhathollo ya yona."	Positive	33
Miyona Ka Bara To Sagwada	Negative	34
Bjale le gapeletša batho ba gabolena gore ba ithekiše, gore le bona re tle re ba rekolle!"	Negative	35
O tsena khutšong; yo mongwe le yo mongwe yo a sepelago ka go loka o khutša lebitleng.	Negative	36
Kafirnya jago jago ya	Positive	37
Mehleng yeo setšhabeng sa geno go tlo phološwa bao ba hwetšwago ba ngwadilwe pukung.	Negative	38
Re ka se tsebe dilo ka moka tšeo barongwa ba di dirago lehono.	Negative	39
Batho le barongwa bao Modimo a ba hlodilego ba be ba ka kgona go kwa Modimo ka mo go phethagetšego.	Positive	40
Ke moka kganyogo ge e gotše e tswala sebe."	Negative	41
Di be di sebelana, "Bošego ke bjoo.	Positive	42
Eupša ba tla thothomela ka letšhogo, gobane Modimo o ema le baloki.	Positive	43
Le gona o tseba bao ba tšhabelago go yena.	Positive	44
Ga go na le modimo wo mongwe wo o ka phološago ka mokgwa wo."	Negative	45
Bona ba re: "Ee, re dihlatse."	Positive	46
Wena o tla bona ge ba kgopo ba fedišwa.	Positive	47
Go pepeneneng gore wa go rereša ke Modimo, le ge batho bohle e le ba maaka.	Positive	48
Yaba ke re: "Modimo wa ka, ako nkgopole ka baka la hona, mme o mpaballe ka baka la boholo ba mohau wa hao."	Positive	49
A ba a re: "Ao ke mantšu a Modimo, mme ke nnete."	Positive	50
Kena Morapedi	Positive	51
Ke moo ba bangata ba tla kgopjwa, mme ba tla ekana, ba tla hloyana.	Negative	52
Di tla dula go yona go ya go ile; di tla dula moo go iša melokong le melokong.	Positive	53
Toko Roti Go	Positive	54
Toko roti Go	Positive	55
Yena o tlo roma Morongwa wa gagwe a go hlahla, gore mosadi wa morwa wa ka o mo tšee gona kua.	Positive	56
Letšatši lela la go šiiša la kotlo ye ba tlilogo re otla ka yona le fihlile, gomme ke mang yo kago e kgotlelela?"	Negative	57
Ge go na le moprofeta gare ga lena,	Negative	58
Botšang ba dipelo tše dinyenyane le re: "Ebang le sebete, le se ke la boifa!	Positive	59
Ge e le dikgoši tša rena, dikgošana tša rena, baperisita ba rena le borakgolokhukhu ba rena ga se ba latela molao wa gago goba go ela hloko ditaelo tša gago, ga se ba latela le dikgopotšo tša gago tšeo o bego o ba kgalema ka tšona.	Negative	60
Ge ba tsena lerung, ba boifa.	Negative	61
O se kgantšhe tša gosasa, gobane ga o tsebe tše di tlago nalo.	Negative	62
Ba tla šireletšwa go iša mehleng ya neng le neng; Eupša ge e le bana ba ba kgopo ba tla fedišwa.	Negative	63
E tla ba bageremi gare ga baditšhaba.	Positive	64
O ka ba kgauswi le Modimo ge feela o mmota e bile o dumela go yena.	Positive	65
Ba mmotšiša ba re: "Ge go le bjalo gona ke mang yo a ka phologago?"	Negative	66
Yena ke moprofeta, mme o tla go rapelela gore o se ke wa hwa.	Positive	67
Ke ofe moporofeta yo botataweno ba sa kago ba mo hlomarela?	Negative	68
Ba pelo tše tshesanyane le ba botše le re: Tiišang!	Positive	69
Mohla nako yeo e fihla batho ka moka ba setšhaba sa geno bao maina a bona a ngwadilwego ka pukung ya Modimo ba tla phološwa.	Positive	70
Lesa go mo tsepelela gore a khutše, Go fihlela a thaba go etša ge mothwalwa a dira bjalo letšatšing la gagwe.	Negative	71
sona se o se holofetšago ba ba go boifago.	Positive	72
ntlo ya Josefa e be lelakabe,	Positive	73
wa otlolla matsogo a gago wa mo rapela;	Positive	74
le Arone yo a bego a mo kgethile.	Positive	75
O ngwadile gore: "Mohlanka wa Morena ga se a swanela go lwa, eupša o swanetše go ba bonolo."	Negative	76
Ntate ke mang ya ka ka wena (Father who can be likened unto you)	Negative	77
O re ntšheditše go re bolaya ka lenyora, rena le bana ba rena le mehlape ya rena?"	Negative	78
Yo a neago modiidi a ka se ke a hloka, eupša yo a utago mahlo a gagwe o tla hwetša dithogako tše dintši.	Negative	79
Are, "Tloho o tlo bona, o ya bona."	Positive	80
Ba ka se hlabje ke dihlong nakong ya masetlapelo, Gomme mohlang wa tlala ba tla khora.	Positive	81
Tšatši le ke go bonago o tla hwa!"	Negative	82
Ee, bao ba tlago go bušwa ke Mmušo wa Modimo ba tla dira thato ya Modimo gomme ba dumela gore Mmopi ke yena a swanelegago go buša batho, e sego motho le ge e le ofe.	Positive	83
Lešaba la buša la kgobokana, moo ba bego ba bile ba sa kgone go ja dijo.	Negative	84
Batho ba gago ba gašane le dithaba gomme ga go na yo a ba kgoboketšago.	Negative	85
Ba otlwa ka kotlo ya mollo wo o sa timego, ya ba sešupo sa go bonwa ke batho.	Positive	86
Fela, tiiša maatla o dire senna ruri ge o hlokomela melao ka moka ye Moshe motseta wa ka a go laetšego yona.	Positive	87
Gomme lešaba la ba ba dumetšego e be e le ba pelo e tee le moya o tee.	Positive	88
ba ke ba theohele Nqalong ya Bafu,	Negative	89
Ya bolela le setšhaba sa yona ya re: "Bonang!	Positive	90
Batho ba bantši ba thekgile Sathane tabeng ya go gana gore Modimo e be Mmuši wa bona.	Negative	91
Bona ba mo fetola ba re: "Ba be ba swana nago - yo mongwe le yo mongwe wa bona o be a swana le morwa wa kgoši."	Positive	92
Ke go agetše ntlo ya bodulo bjo bo phagamego, e lego lefelo la gago leo le tsepamego, gore o dule go lona go iša mehleng ya neng le neng."	Positive	93
Na ga le bana ba karogo, yona peu ya maaka,	Negative	94
Moko wa lentšu la gago ke therešo, Kahlolo e nngwe le e nngwe ya gago ya go loka ke ya go iša mehleng ya neng le neng.	Positive	95
O tseba tsohle gape o gohle.	Positive	96
Wena o na le mantšu a bophelo bjo bo sa felego;	Positive	97
moloki yena o phološwa ke tsebo ya ponelopele.	Positive	98
Na nka se go phagamiše ge o ka fetoga wa dira botse?	Negative	99

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Pedi Sentiment Corpus

Dataset Description

This dataset contains sentiment-labeled text data in Pedi 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: 422,975
Positive sentiment: 255703 (60.5%)
Negative sentiment: 167272 (39.5%)

Dataset Structure

Data Fields

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

Citation

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

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

17.8 MB

Size of the auto-converted Parquet files:

17.8 MB

Number of rows:

422,975

Collection including michsethowusu/pedi-sentiments-corpus

Africa Sentiment Datasets

Collection

The collection contains sentiment-labeled text data for 42 African languages • 43 items • Updated 3 days ago