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:
Nuer
Size:
10K - 100K
License:
Dataset Viewer
Nuer
stringlengths 10
286
| sentiment
stringclasses 2
values |
|---|---|
tfraiau tfiraua tfiraau tfiruaa tfiruaa tfiraua tfiraau tfiarua tfiarau tfiaura tfiauar | Negative |
Bëë nhial kiɛ bëë kä naath?" | Negative |
Cu kun gat gam kɛ tetkɛ. | Positive |
Kɛ kwic dwelli. tee kɛɛ wutni ti görkɛ lat dweel. | Positive |
Dualɛ kɛ Kuoth. | Negative |
?Zezi waan ɔ fin nin? ?Yɛ i sɔ'n kle kɛ ɔ ti wan wa? | Negative |
Luɔnyɛ jɛ kä nööŋɛ jɛ wanɛ mɛ. | Positive |
a mì si Yɛnŋɛlɛ sɛnrɛ ti yo wa Damasi ca gbɛn, | Negative |
Tag: thue tham tugia thue tham tuchi phi thu tham tudich vu tham tutham tu uy tin chuyen nghiep | Negative |
Kä cukɛ jɛ tok kɛ mi görkɛ jɛ rɛy watnikiɛn kɛnɛ mäthnikiɛn. | Positive |
Jaopa jɛ cɔl nhial, cuɛ wee, "Abɛraam! | Positive |
jɛ jiök, "Gaat muɔɔr cikɛ jiɛɛn wanɛmɛ kɛ | Positive |
Shi wɔbiɔ saji yɛ shihilɛ kɛ bɔ ni nibii baaji wɔsɛɛ lɛ hu ahe. | Positive |
Muaj ntau tej yaam num hu rau qhov chaw tej yam khoom; | Negative |
cak ni kɛ jɛ. | Positive |
wee, "Mi wä yɛn wä röm kɛ dämaar ni Ithɔɔ, | Positive |
Kä niɛ guäth in ca ruac liŋ, cu Cɛy-tan ben, cuɛ ruac in ca piɛth rɛydiɛn ben woc. | Negative |
Kä cuɛ Muthɛ lath kɛ dual kɛ guecdɛ. | Negative |
Yɛn diaal, nyuɔɔrɛ! | Positive |
iij mqeiij mqaiij mqiiij mqoiij mqdiij mqhiij mqniij mqriij mqsiij mqtiij mquiij mqyiij mqciij mqfiij mqgiij mqliij mqjiij mqmiij mqwiij mqbiij mqviij mqkiij mqxiij mqpiij mqqiij mqz | Negative |
Acaan ɛkɛ 'ya muona bora noŋ kanŋa ɛkɛ neeni, ɔci 'ya 'wonna ɛkɛ war tɛɛn ɛkɛ noŋa kanŋa ɛkɛ neeni. | Positive |
Vɛɛ Chiisu ndoo che yɛ masale chieeŋndeŋ? | Negative |
Pe pe yɛɛ yigi kɛɛnrɛ limɛ pi ni." | Negative |
Cuaa Jɛykɔp jiök, i "Ci gatdu ni JOthɛp ben | Positive |
Cïn muöl cäth, tuk wälä diër rin yïn rot läc; | Positive |
Barɛ ro päämni mäni mi yiek yɛ | Negative |
kuɛn piny nhiam nɛɛni diaal tëë cuŋ kɛ jɛ | Positive |
"Ki naŋa ŋa wì pye mɛlɛ mɛɛ Yɛnŋɛlɛ sɛnrɛ ti jɛn yɛɛn, | Positive |
Täämɛ cuɛ Jɛykɔp lɔcdɛ coo waŋ kɛ Lɛybɛn, | Positive |
Kä cuɛ jɛ jiök, "E jɛn mäni | Positive |
Kä bia dɛy kuir rɛy lätni diaal ti gɔw, kä bia piith rɛy ŋäcä Kuɔth. | Positive |
Ram mi lät mi gɔaa ɛ raan Kuɔth. | Positive |
naa tire nuwɔ taan cɛnlɛ pyew ti ni, | Negative |
?Kɛ Mari seli Zozɛfu kɛ w'a wunnzɛ'n, akunndan benin yɛ Zozɛfu buli-ɔ? ?Yɛ ngue ti-ɔ? | Negative |
Nyɔŋmɔ miisumɔ ni wɔhi shi yɛ toiŋjɔlɛ kɛ miishɛɛ mli yɛ paradeiso mli yɛ shikpɔŋ lɛ nɔ kɛya naanɔ! | Positive |
Noa nabi, kɛ Ham binuu ni ji ejwɛ nɔ. | Negative |
Kä a liak tekɛ jɛ a thil pek. | Positive |
Ka mɛni pɛlɛ-pɛlɛ ŋa kɛ kákîe-ni mì nyii a kátûalaai su kpanáŋ | Positive |
Lere ŋa fuun kɔɔn kɛɛ yaraga shɔ, | Positive |
iij nieiij niaiij niiiij nioiij nidiij nihiij niniij niriij nisiij nitiij niuiij niyiij niciij nifiij nigiij niliij nijiij nimiij niwiij nibiij niviij nikiij nixiij nipiij niqiij niz | Positive |
tu thu allaiKiaal uf thu calalu. | Positive |
Kä ram mi jiäk bä lät ti jiäk raar rɛy lɔaacdɛ mi jiääk. | Negative |
Na dat na mi nem; ɛn a nɔ de gi mi glori to ɛnibɔdi ɛn mi prez to ɛni aydɔl." | Positive |
Kä ram mi näk raan kɛ thɛp, ba jɛ dhil näk kɛ thɛp." | Negative |
tɔ lam ji tɔ, kä poth a kɛ nɛy tɔ puɔth | Positive |
"La nɛy nyin tɛthkä lɔaac a lät kɛɛl." | Positive |
Mɛni abaasusu he yɛ yitso ni nyiɛ sɛɛ lɛ mli? | Negative |
Mi lätdi mi jiääk, lätdi jɛ kä rɔɔdu, | Negative |
cian awk jie! | Positive |
ɛniɛ safety first | Positive |
Eyɛ mli akɛ Nyɔŋmɔ hala hii ni yeee emuu akɛ enajiaŋdamɔlɔi moŋ, shi mɛni ekpa gbɛ akɛ Israelbii lɛ baafee? | Negative |
katugu mi yɛn na jɛngɛ lɛgɛrɛ jɛɛn, | Negative |
jɛ, "Yɛn wuɔcɛ kuth kɔkiɛn tɔ tee kɛ yɛ tɔ, | Positive |
ua qasurat bî a"mâlî, ua qa"adat bî aglâlî, | Positive |
mɛnni kɛ kelen fɛ. | Positive |
cätdan, banɛ jɛ cal kɛ cätdan, kä jɛn bɛ | Positive |
gääm ɛ jiök, "'Cu dual. | Positive |
Ni kɛ ebalɛ akɛ amɛyaje haomɔ ko mli lɛ, wɔbaaye wɔbua amɛ ejaakɛ wɔji weku agbo diɛŋtsɛ. | Positive |
Cuɛ wee, "Nɛn ɛ, mac kɛnɛ | Positive |
Vɛɛ naŋ tuu yɛ chieeŋ naŋ cho hoo niŋndo a chieeŋndo o teleŋ masindɔɔ niŋ, nduyɛ nyuna yɛɛ fula yɛ a hei okɔɔ? | Negative |
Ani nɛkɛ ji bɔ ni Nyɔŋmɔ to eyiŋ kɛha mi kɛ adesa weku muu lɛ fɛɛ? | Negative |
lɔcdɛ thɛm, cuɛ jɛ jiök, "Abɛraam." | Positive |
Mɛɛ gbɛ nɔ wiemɔ ni ji 'tsi ohe kɛje nii ni ejaaa he' lɛ kɔɔ nibii komɛi ni tee nɔ yɛ Mose beaŋ lɛ he? | Positive |
Sane ni wɔshiɛɔ kɛ hiɛdɔɔ, | Positive |
hnayuua hnauyau hnauyua hnauayu hnauauy hnauuay hnauuya hnaauyu hnaauuy hnaayuu hnaayuu | Negative |
kä kɔn mi dee niɛɛn kɛ kɔn. | Negative |
huhu, tu ayt al-quran yg bwk mkne cam nie, | Positive |
Bikɛ kuth ti jiäk woc kɛ ciötdä, kä bikɛ ruac kɛ thuk ti gööl. | Negative |
Yoj wuquʼ wä qachʼalal qiʼ, chqä taq kʼa yin koʼöl na kʼïy xintamaj chrij ri samaj pa tikoʼn. | Positive |
ŋic thöpä läri ɛ tämɛ kɛ ŋieckɛ nath kɛ kä UK kɛ liw. | Positive |
Mɛni aboloo kɛ wein ni akɛtsuɔ nii yɛ Nuŋtsɔ lɛ Gbɛkɛ Niyenii lɛ shishi lɛ damɔ shi kɛha? | Negative |
ʼat ni tzij pawi, ca ʼiltaj ʼuri we ʼo i a mac, | Negative |
Ci kɛ de ben kɛ cäŋ kuoth kiɛ cäŋ lɔŋ kä wec kɛ liw. | Negative |
ji poth, bä ram ɔ lam ji mɔ lam, kä thär | Positive |
kalo meiyra cuyaa ? | Negative |
I wanaaa win this!:D | Positive |
Nga sanni kɛnin kɔ, | Positive |
gaatkɛ Cɛm, Hɛm kɛ Jɛpɛth, kɛ ciek Nowa, kɛ | Positive |
su ui iv aytujej nuiiiuiJiicj uia- - | Positive |
Kɛ kɛn bɛl. | Negative |
Cu Kuoth ɛ nɛn, cɛ gɔaa. | Positive |
täämɛ yɛn 'cuayɛ mɛ jiääk ɛmɛ lät. | Negative |
?Amun kunndɛ kɛ amún sí ndɛ ng'ɔ ti nanwlɛ'n? | Negative |
a wì suu yɛɛra yaripɔrɔ ti lɛ mari le, | Positive |
Kä baa jɛn raam min baa | Positive |
same askɛ diɛwɛadj | Negative |
Fɔli, mɛni he hia nyɛ bimɛ ɔmɛ nɛ ma ha nɛ a ná bua jɔmi ngɛ a si himi mi? | Negative |
Eric, ɛkulo kɛ ɛkenga ɔ? | Negative |
Cu dämani coo ruac kɛ jɛ kɛ kɔrɛ. | Positive |
Re tjonïk reʼ xtqrtoʼ rchë ma xtqaqʼäj ta rutzij Jehová taq xtkitäj kiqʼij chqij rchë yeqaʼän costumbres chrij ri kamïk. | Positive |
thil riɛk kɛ ti diaal tëë te tetkä Jothɛp | Positive |
nmtcauu nmtcauu nmtucua nmtucau nmtuuca nmtuuac nmtuauc nmtuacu nmtuuca nmtuuac nmtucua | Positive |
Afee ŋwɛi ehee, Satan bɛ jɛi dɔŋŋ; | Negative |
Sokere: To eŋɛ la wani baŋɛ ti Naayinɛ keleseri to? | Negative |
Mi lätdi duɔɔr mi gɔaa, lätdi jɛ kä rɔɔdu, | Positive |
tu waqt tha. | Negative |
Cu wut ɛmɔ cuaa riäŋ riäŋ. | Positive |
rɔ yuɔr piny nhiamdɛ, cukɛ wee, "Nɛn ɛ, kɔn | Positive |
Kä cukɛ Kuoth I-thɛ-rɛl liak. | Positive |
He ni wɔshɛ yɛ Satan je lɛ mli, wɔkɛ naagbai baakpe. | Negative |
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Nuer Sentiment Corpus
Dataset Description
This dataset contains sentiment-labeled text data in Nuer 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: 12,525
- Positive sentiment: 7538 (60.2%)
- Negative sentiment: 4987 (39.8%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Nuer
- 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/nuer-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 Nuer
- Cross-lingual sentiment analysis research
- African language NLP model development
Citation
If you use this dataset in your research, please cite:
@dataset{nuer_sentiments_corpus,
title={Nuer Sentiment Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/nuer-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:
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Size of the auto-converted Parquet files:
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Number of rows:
12,525