Africa Sentiment Datasets
Collection
The collection contains sentiment-labeled text data for 42 African languages
•
43 items
•
Updated
Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Tsishingini
Size:
100K - 1M
License:
Dataset Viewer
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
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Size of downloaded dataset files:
36.1 MB
Size of the auto-converted Parquet files:
36.1 MB
Number of rows:
877,198