Datasets:

michsethowusu
/

tswana-sentiments-corpus

Tswana stringlengths 7 497	sentiment stringclasses 2 values	__index_level_0__ int64 0 877k
Moperofeti le bapati ba gagwe ba le mmalwa ba ne ba le mo kgolegelong dikgwedi.	Negative	0
Dafita a direla metse yotlhe ya Baamone jalo.	Positive	1
mme go se na mothusi ope;	Negative	2
Letsatsi la rona la loso le ka nna botoka thata jang go na le letsatsi le re tshotsweng ka lone?	Negative	3
A bo re itumela jang ne gore Modimo wa boammaaruri ga a maatla otlhe fela e bile ga a botlhale jo bogolo fela mme gape o ikokobeditse!	Positive	4
Foo o tla direla leina le legolo la gago eng?"	Positive	5
"A go na le Modimo kwa ntle ga me?	Negative	6
Mosiami o sianela mo go yone mme o a sirelediwa."	Positive	7
Go boima go emela Modimo.	Negative	8
O ne a re: "O sa ntse o le mosha e bile o na le bokamoso jo bo molemo.	Positive	9
O ka šutela ga Modimo mo o mo tshepa fote o dumela gage.	Positive	10
Maitsholo mangwe le mangwe a a duleng mo tseleng a tla bona kotlhao e e gagametseng.	Negative	11
Ba bo ba tswaletswe jalo go fitlha letsatsing la loso lwa bone, ba dirwa jaaka batlholagadi bophelong jotlhe jwa bone.	Negative	12
Gape ga go kgathalesege gore o fitlheletse jang lefatshe.	Positive	13
Mme boammaaruri jo bo ntle ja mafoko a a molemo ke gore Modimo o rata batho ba ba latlhegileng jalo.	Positive	14
mme dipelo tsa bone di kgakala le nna.	Negative	15
metsi a se ke a tlola taelo ya hae;	Positive	16
"Batho ba me ba ba tlhophilweng ba tla dirisa tiro ya diatla tsa bone ka botlalo.	Positive	17
Mme go na le ba bararo ba ba nayang bosupi mo lefatsheng."	Positive	18
Re lositse ka lone Kapa a utlwa,	Positive	19
o leboga Modimo; mme yo o sa jeng, ga a	Positive	20
Ke Ntaba re Tshwantse go Leboga?	Positive	21
e ba wela hodimo hakae,	Positive	22
Ke tla e naya monna wa Modimo gore a re itsise tsa leeto la rona."	Positive	23
Wena Juda, ke etse jwang ka wena?	Positive	24
O ne a re: "Dilo tsotlhe di kafa molaong; mme ga se dilo tsotlhe tse di agang.	Negative	25
A kampo o jele setlhare se ke go laetseng ka re: O se ka wa se ja?"	Negative	26
Fa o dira jalo, o tla tlhaloganya se Modimo a ikaeletseng go se direla batho.	Positive	27
Le fa go ntse jalo, Solomone o nyala basadi ba le bantsi ba ba tswang kwa dinageng tse dingwe mme ba sa obamele Jehofa.	Negative	28
Mme Tiiso ya Botshelela ha mmoho ke Buka, maqephe a tsheletsng feela mmoho; mme e bula Buka ya Topollo.	Negative	29
Batlodiwa ba itshupile jang e le 'batho ba leina la ga Jehofa' gompieno?	Negative	30
Fa ba sena go ja, baaposetoloi ba ne ba ngangisana gore ke mang yo mogolo.	Positive	31
Tsaya kgato gone jaanong, ka gonne masego a Bogosi jwa Modimo a gaufi!	Positive	32
ka bowena ntle le thuso ya babang. (mm. boikgutliso, kgutliso, kgutlisetso, kgutlisoa,	Negative	33
Hee! batho ba tlaa golofala!	Negative	34
Solomone o ne a re: "Latlhela senkgwe sa gago mo godimo ga metsi, gonne o tla se bona gape morago ga malatsi a mantsi."	Negative	35
A ke batla go ikamanya le batho ba ba dirang tiro ya go rera dikgang tse di molemo tsa Bogosi jwa Modimo mo lefatsheng lotlhe?'	Negative	36
A ga a dira tiro le bone boikarabelo jwa gagwe ka go go godisa ke jaaka o le monna jaana gompieno?"	Negative	37
Le gona ga re na boswa mo go morwa Isai.	Negative	38
Ka letsatsi leo, Modimo o tla fedisa baikepi botlhe.	Negative	39
mme tsohle tseo le di kopang ka thapelo, le dumela, le tla di fumana."	Positive	40
ke mo latlhela mo bolaong jwa bolwetse; ba ba dirang boaka nae ke tla ba latlhela mo	Negative	41
Kwa re tla nnan le ka bosakhutlen;	Positive	42
Letsogo la gagwe le lolame, mme ke mang yo o tlaa le gonang.	Negative	43
A o ka rata go itse mo go oketsegileng ka Bogosi jwa Modimo le se bo tla se dirang?	Negative	44
Ba mmotsa ka tsa setso le borapedi;	Positive	45
Seno se ne sa hutsafatsa Lote ka gonne e ne e le monna yo o siameng.	Positive	46
go bo go latela nomorotshupetso ya gago e e rebotsweng ke kantoro;	Negative	47
Ha ana Kereke tse pedi ka nako ele nngwe.	Negative	48
Empa Modimo o se ke wa mo araba letho tsatsing leo.	Negative	49
Jaanong ditiro tsa bona di ba dikanyeditse, di fa pele ga sefatlhego sa me.	Negative	50
mme a e nne ka ditiro tse di molemo - e le mo go tshwanetseng basadi ba ba ipalang ka poifomodimo.	Positive	51
Ga go na Modimo ope fa e se Wena, Thuso mo Diphatseng, O O Tshelang ka boOne.	Positive	52
Kgotsa fa a lopa tlhapi, o ka mo naya noga mo boemong jwa tlhapi?	Negative	53
re batho ba ga bo leshushumela motlishi kgauswi le motse wa setumong,	Negative	54
Ga go ope Modimo fa e se Wena, Thuso mo Diphatseng, O O Tshelang ka boOne.	Positive	55
Ke eng fa bomogoloe Josefa ba ne ba sa kgone go bua le ene ka kagiso?	Negative	56
O ne o ikanyega mo dilong di le mmalwa.	Positive	57
Modimo e atametse."	Positive	58
Hobaneng nka shwellwa ke lona ba babedi ka tsatsi le le leng?"	Negative	59
Seatla sa gago se nonofile, seatla sa gago sa moja se godisitswe."	Positive	60
Gonne kajeno molato wa Baamori ga o ise o tlale."	Negative	61
Ya re: "Itumeleng, lona magodimo le lona ba lo agileng mo go one!"	Positive	62
bogolo go maago otlhe a ga Jakobe.	Positive	63
kwa Macheng, mme a bolailwe ke letsatsi; ga rea diha re robile sepi.	Negative	64
A re: "Mosadi yo, o letse a ntheile a re, ke ntshe morwaake gore re mo je kajeno; morwawe re tla mo ja kamoso.	Positive	65
'MENE e raya gore Modimo o badile malatsi a bogosi jwa gago mme o bo fedisitse.	Negative	66
O tla ba mmalehi le moleleri lefatsheng."	Positive	67
Lefatshe le Lesha - A o Tla Nna Gone?	Positive	68
Jaanong a ko lo dire kgolagano le rona."	Positive	69
"Gonne ga o ketla o obamela modimo ope o sele; gonne Jehofa yo leina la gagwe go tweng, Kelefufa, ke Modimo o o lefufa ruri:	Negative	70
Mme e bile ga ba buwe diphologolo tsa bone,	Positive	71
Gone jaanong, tota le batho ba ba lekang go itumedisa Modimo ba tla tlhobaela.	Negative	72
Jaanong he, fa go le bosula mo matlhong a gago, nte ke boe."	Negative	73
Ga go ope Modimo fa e se Wena, O O Osi, O O Senang Tshwantshanyo, Moitse-Gotlhe, Motlhale-Otlhe.	Positive	74
Go tswa foo, o se ka wa itlhaganelela go lebala se o se boneng.	Positive	75
Akanya gape ka monna yo o ikanyegang e bong Jobe yo o neng a re: "Ke dirile kgolagano le matlho a me.	Positive	76
Ngavhe a tshi pfa tshililo nga matsheloni,	Positive	77
porofeto) ke sesupo go ba ba dumelang, e seng go ba ba sa	Positive	78
A Botshelo Jo bo Sa Khutleng Kwa Paradaiseng bo tla bo bo Lapisa?	Negative	79
O na le maatla, gape'o botlhale;	Positive	80
E bile gongwe o kgatlhilwe ke masego mangwe a mo isagweng a Bogosi jwa Modimo a go tlotlilweng ka one.	Positive	81
Mme Beata Pach;	Positive	82
O dirile ka tshiamo le ka tshwanelo; ruri, o tla phela.	Positive	83
Mme Nguyen (Thi Thao),	Positive	84
Kana o ne a tle a re le ba ba marokgwe ba, o ba bone, ba tshwana fela le ba mesese ka go seba.	Negative	85
Empa badumedi baile batswa mme ba kena lefatsheng la tshepiso; mme baile bakena tlasa molaetsa wa tlotsuo.	Negative	86
bonang, letsatsi le e tla le le tukang jaaka leiso, mme botlhe ba	Positive	87
Mme Naomi o ba raya a re: 'Lo tshwanetse go boa, barwadiake.	Positive	88
Hesekia a araba a re: "Ba bonye tsotlhe tse di mo ntlong ya me.	Positive	89
Re ne ra re, thobane e, ke yone lore; ke yone seikokotlelo, selo se se ikokotlelang.	Negative	90
A ga re leboge?	Negative	91
ba tla swa ba se na kitso.	Negative	92
E re ka Noa e ne e le "moreri wa tshiamo," o ne a rerela batho molaetsa o a neng a o neilwe ke Modimo.	Negative	93
A ke nnete kgotsa ke tlhamane fela?	Negative	94
Modimo o neetse Bamidiane le mephato yotlhe mo diatleng tsa gagwe."	Positive	95
Modimo o ne a bopa eng mo letsatsing la boraro la popo?	Positive	96
"O tla golola mohumanegi yo o goang a kopa thuso, le yo o bogisiwang le ope fela yo o se nang mothusi.	Positive	97
Ke Ene yo o tlisang masomosomo a tsone ka palo, a di bitsa tsotlhe ka maina.	Positive	98
ba tsenye mo bosweng jwa gago;	Positive	99

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Tswana Sentiment Corpus

Dataset Description

This dataset contains sentiment-labeled text data in Tswana 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: 877,198
Positive sentiment: 519057 (59.2%)
Negative sentiment: 358141 (40.8%)

Dataset Structure

Data Fields

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

Citation

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

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

36.1 MB

Size of the auto-converted Parquet files:

36.1 MB

Number of rows:

877,198

Collection including michsethowusu/tswana-sentiments-corpus

Africa Sentiment Datasets

Collection

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