Datasets:

michsethowusu
/

lingala-sentiments-corpus

Lingala stringlengths 1 484	sentiment stringclasses 2 values	__index_level_0__ int64 0 428k
Bongo atunaki bango: 'Mpo na nini botelemi awa mokolo mobimba mpe bozali kosala eloko te?	Negative	0
Nalobi epai ya bino ete, iyo; nzokande bokoli te o boyebi boko bobongo nie.	Negative	1
Mpe soki mokomoko azali na mokumba, akomona ete losambo na kati ya libota etaleli mpe ye.	Positive	2
Mpe tala, lisusu, balobaki eye etindamaki bango na Nkolo; yango wana, mazali ma sembo mpe ma solo.	Positive	3
Pamba te, bakoyoka kaka ete osi'okomi awa!	Positive	4
Mityelá mokano ya kozwa eloko oyo ezali na litomba mingi koleka bozwi ná biloko ya kobikela.	Positive	5
Atako mana ezalaki likabo oyo eutaki epai ya Nzambe, epesaki bato bomoi ya seko te.	Negative	6
Nakotya yo obatela biloko mingi."	Positive	7
Ndenge nini toyebi ete Nzambe andimaka te ndenge nyonso ya kosambela ye?	Negative	8
Tala, nkoma izali liboso lya bino; soko bokokamola yango ekozala mpo ya bobebisami bwa bino moko.	Positive	9
Banda vi banaya bayimaan hai loko...	Positive	10
Mpe akopika bahema na ye oyo ezali lokola bandako minene kati na mbu monene mpe ngomba mosantu ya Kitoko; mpe akoya tii na nsuka na ye, mpe akozala na mosungi te."	Negative	11
Olingi koyeba makambo mosusu etali Bokonzi ya Nzambe mpe oyo ekosala?	Negative	12
Kasi ateyaki mpe bango bábondelaka ete Bokonzi ya Nzambe eya mpe bázelaka ntango yango na motema esengo.	Positive	13
Na ndakisa, Biblia esaleli liloba lobɔkɔ mbala ebele.	Positive	14
Na kozela banzelu bazongaki mpe na boboto batunaki ye, "Mwasi, ntina nini ozali kolele?	Negative	15
Yango ekosalisa yo oyeba oyo Yehova azali kosɛnga yo mpe ndenge oyo asalisaki basaleli na ye na kala.	Positive	16
Pamba te apesaki motindo mpe yango ekelamaki.	Positive	17
Moyengebene akimaka mpo na kokɔta kuna mpe azwaka libateli."	Positive	18
Litomba oyo okoki kozwa: Nzambe alaki ete soki oyebi ye mpenza malamu, okozwa bomoi ya seko.	Positive	19
Na yango, tokomonisa bwanya soki tosaleli makoki na biso ya kokanisa mpe tobakisi boyebi na biso ya Liloba ya Nzambe.	Positive	20
Mpo soko ezali moko o ntei ya bino oyo asali bolamu, akosala na nguya mpe makabo ma Nzambe.	Positive	21
Okoki kopusana penepene na Nzambe kaka soki otyeli ye motema, mpe soki ondimeli ye.	Positive	22
Natatoli lisusu ete tokoki te kokumbama libanda soki tokolanda toli ya profeta ya Nzambe.	Positive	23
Ntango "mokolo ya kosambisama" ya Nzambe ekoya, baoyo baponi kozala banguna ya Nzambe bakobebisama ndenge wana.	Negative	24
Eyebisaka bango ete bazali bana ya Nzambe mpe ete basengeli kolya limpa mpe komɛla vinyo	Positive	25
Kasi, elimboli ete baoyo bakumisaka mpenza nkombo ya Nzambe bakoki kobelela ye ata na ngonga nini mpo na kozwa libateli.	Negative	26
Nzambe atalelaki yango na ndenge ya malamu mpo asala ndenge asali na mokolo ya lelo: kobatela bato mingi na bomoi."	Negative	27
Soki moto moko nde asalá Nzambe, moto yango asengeli kozala Mozalisi.	Negative	28
Boye okotambola na kimya na nzela na yo, mpe ata lokolo na yo ekotutana na eloko moko te."	Positive	29
To kofandisa bango na se ya mayi ti ba kokufa kuna.	Positive	30
Ndenge nini toyebi ete bato nyonso oyo bazwaki elimo ya Nzambe baponamaki te mpo na kokende likoló?	Negative	31
Na nsima mposa wana, ntango ezwi zemi, eboti lisumu."	Negative	32
Ye Oyo Aleki Likolo azali na boyebi?"	Positive	33
mpe bakopesa ye kombo Emanuele;	Positive	34
Na nsima mposa wana, ntango ekoli, eboti lisumu."	Negative	35
Na Nzambe azali moyebi wa eloko inso ."	Positive	36
le lon ponana ye paroranga ki,	Negative	37
Sima na yango, bakobima na mokili yango mpe bakosambela ngai na esika oyo."	Positive	38
Ntango azalaki awa na mabele, atikalaki sembo tii na liwa.	Positive	39
Mpo na nini Nzambe abundelaki Bayuda te ndenge asalaki yango kala?	Negative	40
Tala, nalobi epai ya bino, ete ezali o ngambo yoko lokola ezali o esusu; mpe ekozala epai ya moto engebene na mosala mwa ye.	Positive	41
Emonani na misala na bango ete babosanaki te libula na bango, to makambo oyo bateyaki bango.	Positive	42
Moto akokana, nzambe akosukïsa (feat.	Positive	43
Moize amonisaki bolingo mpo na Nzambe mpe mpo na baninga na ye Bayisraele.	Positive	44
Elinga Nzambe ete ekoka kozala o mikolo mya ngai; kasi yango ezala mosika te to mosika, o yango nakosepela.	Positive	45
le titakrai ye mai san,	Positive	46
Bokanisi batongi yango na mayi esika bato ya mboka bakokoka kokende te pamba?	Negative	47
Andimaka moto nyonso oyo alingi kosambela ye.'	Positive	48
O bandimi! tango nini bokososola?	Positive	49
Na yango, atikelá basaleli ya Nzambe lelo oyo ndakisa moko malamu.	Positive	50
Mpo na sikoyo, ata baoyo basalaka nyonso mpo na kosepelisa Nzambe bakutanaka na mitungisi.	Negative	51
Maze Bakoseli Ngai Makambo Mpo Nakosa Na Wele,	Negative	52
Ndako nyonso oyo bakokuta mopɛngwi, esengelaki kobebisama."	Negative	53
Azalaki koloba na basaleli na ye na boboto, mpe na ndenge oyo ebongi mpenza.	Positive	54
Pamba te bato ya masumu mpe basalaka bongo.	Negative	55
Boye, bakosala makambo na bwanya ata soki ozali te.	Positive	56
Na yango, "Goge" oyo mokanda ya Ezekiele to Emoniseli elobeli, ezali Satana te.	Negative	57
Bongo alobaki na ngai: Ezali baoyo bawuti konyokwama mingi penza.	Positive	58
Nini ekosalisa yo ntango ozali kosolola na basusu mpo na Nzambe mpe mpo na bozalisi?	Negative	59
Mibembo mya ye o kati ya esobe, mpe bongo na bongo.	Positive	60
Yakobo akendaki liboso ya libota na ye, mpe afukamaki mbala nsambo liboso ya ndeko na ye.	Positive	61
Batu oyo basili kosala malamu, bakosekwa mpo na kozwa bomoi.	Positive	62
Na nsima, Nzambe 'akopusana penepene na yo.'	Positive	63
Ata bongo, bolinganaka mpe bolingi te kosalana mabe.	Positive	64
Tótalela mpe ndakisa ya Yobo, moto moko ya sembo oyo alobaki: "Nasalá kondimana na miso na ngai.	Positive	65
Mpe nalobi epai ya bino ete babikisamaki.	Positive	66
Bango nde ba ko lamusaka biso, tongo esi etani	Positive	67
Bamosusu balobaki ete: "Liloba oyo ezali makasi; nani akoki koyoka yango?"	Negative	68
Tango nini okopesa etumbu na bato oyo bazali konyokola ngai?	Negative	69
To owanganaki kondima?'	Negative	70
Liloba ya Nzambe ekebisi biso ete: "Bókangana te na ekanganeli ya mabe esika moko na bato oyo bazali bandimi te.	Positive	71
Bongo bokengeleke, zambi boyebi ata mokolo ata ngonga te."	Negative	72
Kasi mpo na baponami, ba oyo ye apona, yango wana atiaki yango mokuse.	Negative	73
Lokola basi ya mibu nyonso, totambolaka kati ya pole na Ye .	Positive	74
Moto nyoso akotosa te, akozwa etumbu ya makasi;	Negative	75
Yebisá bato ete bámeka komata na Ngomba Sinai te.'	Negative	76
Kutu, akebisaki nde bakristo boye: "Bókangana te na ekanganeli ya mabe esika moko na bato oyo bazali bandimi te.	Positive	77
Pamba te, ye awutaki na suka ya mokili mpe ayaki mpo na koyoka bwanya ya Salomo.	Positive	78
Abatelaka basambeli na ye.	Positive	79
Tomonaki bondimi na bango mpe lolenge bazalaki kosalela yango.	Positive	80
Alobaki ete: "Kaka ndenge Tata ateyaki ngai, ndenge mpe nazali koloba."	Positive	81
Nakomonisa bango ete bazali na libunga.'	Negative	82
Nzambe azali na nguya oyo ezangi ndelo mpe azali na mposa ya kosalela yango mpo na bolamu na biso.	Positive	83
Alakaki ete bana ya Yakobo bakokóma ekólo moko ya nguya.	Positive	84
Nzambe na sengi se na yo ooo, bomengo oo bwanya mayele eeee,	Negative	85
Kaka ntango bandeko mosusu bazongaki na ntɔngɔ makasi nde nakutanaki na bango."	Positive	86
Kasi abatelaki kaka Nowa oyo azalaki koteya bosembo, mpe bato sambo mosusu elongo na ye.	Negative	87
Sikoyo, kanisá ete baanzelu bazali koyebisa yo: "Kondima lokuta ya Satana te."	Positive	88
Akomaki boye: "Nani akokoka na mokolo ya koya na ye, mpe nani akotɛlɛma ntango ye akomonana?	Negative	89
Mais bakosambua.	Positive	90
Ye ayebi makambo nyonso!	Positive	91
Kutu, 'asepelaka kolimbisa.' - Nz.	Positive	92
Zuwa na bango etindaki bango básala makambo oyo na nsima, epesaki bango mpasi mingi na motema.	Negative	93
mateya ya solo oyo ateyá yo?	Positive	94
Oyaki kososola ete bato nyonso bazali mosika na Nzambe.	Negative	95
Makomami emonisi mpe ete bamonisaki mpenza botosi oyo esengeli na likambo yango.	Positive	96
ya til des signes ou pas ?	Negative	97
Totunaki bango boye: "Mpo na nini bakristo basambelaka Yesu, ekulusu, Maria, mpe bikeko mosusu nzokande Mibeko Zomi epekisi yango?"	Negative	98
Asalaki mokili wuta na mayi mpe na nzela ya mayi.	Positive	99

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Lingala Sentiment Corpus

Dataset Description

This dataset contains sentiment-labeled text data in Lingala 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: 427,979
Positive sentiment: 251923 (58.9%)
Negative sentiment: 176056 (41.1%)

Dataset Structure

Data Fields

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

Citation

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

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

17.7 MB

Size of the auto-converted Parquet files:

17.7 MB

Number of rows:

427,979

Collection including michsethowusu/lingala-sentiments-corpus

Africa Sentiment Datasets

Collection

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