Africa Emotion Datasets
Collection
The collection contains emotion-labeled text data for 42 African languages • 45 items • Updated
Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Nuer
Size:
10K - 100K
License:
Nuer stringlengths 10 286 | emotion stringclasses 7 values |
|---|---|
tfraiau tfiraua tfiraau tfiruaa tfiruaa tfiraua tfiraau tfiarua tfiarau tfiaura tfiauar | sadness |
Bëë nhial kiɛ bëë kä naath?" | neutral |
Cu kun gat gam kɛ tetkɛ. | surprise |
Kɛ kwic dwelli. tee kɛɛ wutni ti görkɛ lat dweel. | joy |
Dualɛ kɛ Kuoth. | fear |
?Zezi waan ɔ fin nin? ?Yɛ i sɔ'n kle kɛ ɔ ti wan wa? | neutral |
Luɔnyɛ jɛ kä nööŋɛ jɛ wanɛ mɛ. | neutral |
a mì si Yɛnŋɛlɛ sɛnrɛ ti yo wa Damasi ca gbɛn, | neutral |
Tag: thue tham tugia thue tham tuchi phi thu tham tudich vu tham tutham tu uy tin chuyen nghiep | neutral |
Kä cukɛ jɛ tok kɛ mi görkɛ jɛ rɛy watnikiɛn kɛnɛ mäthnikiɛn. | neutral |
Jaopa jɛ cɔl nhial, cuɛ wee, "Abɛraam! | neutral |
jɛ jiök, "Gaat muɔɔr cikɛ jiɛɛn wanɛmɛ kɛ | neutral |
Shi wɔbiɔ saji yɛ shihilɛ kɛ bɔ ni nibii baaji wɔsɛɛ lɛ hu ahe. | neutral |
Muaj ntau tej yaam num hu rau qhov chaw tej yam khoom; | disgust |
cak ni kɛ jɛ. | neutral |
wee, "Mi wä yɛn wä röm kɛ dämaar ni Ithɔɔ, | anger |
Kä niɛ guäth in ca ruac liŋ, cu Cɛy-tan ben, cuɛ ruac in ca piɛth rɛydiɛn ben woc. | disgust |
Kä cuɛ Muthɛ lath kɛ dual kɛ guecdɛ. | fear |
Yɛn diaal, nyuɔɔrɛ! | neutral |
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 | anger |
Acaan ɛkɛ 'ya muona bora noŋ kanŋa ɛkɛ neeni, ɔci 'ya 'wonna ɛkɛ war tɛɛn ɛkɛ noŋa kanŋa ɛkɛ neeni. | joy |
Vɛɛ Chiisu ndoo che yɛ masale chieeŋndeŋ? | neutral |
Pe pe yɛɛ yigi kɛɛnrɛ limɛ pi ni." | neutral |
Cuaa Jɛykɔp jiök, i "Ci gatdu ni JOthɛp ben | neutral |
Cïn muöl cäth, tuk wälä diër rin yïn rot läc; | neutral |
Barɛ ro päämni mäni mi yiek yɛ | fear |
kuɛn piny nhiam nɛɛni diaal tëë cuŋ kɛ jɛ | neutral |
"Ki naŋa ŋa wì pye mɛlɛ mɛɛ Yɛnŋɛlɛ sɛnrɛ ti jɛn yɛɛn, | neutral |
Täämɛ cuɛ Jɛykɔp lɔcdɛ coo waŋ kɛ Lɛybɛn, | neutral |
Kä cuɛ jɛ jiök, "E jɛn mäni | surprise |
Kä bia dɛy kuir rɛy lätni diaal ti gɔw, kä bia piith rɛy ŋäcä Kuɔth. | neutral |
Ram mi lät mi gɔaa ɛ raan Kuɔth. | neutral |
naa tire nuwɔ taan cɛnlɛ pyew ti ni, | sadness |
?Kɛ Mari seli Zozɛfu kɛ w'a wunnzɛ'n, akunndan benin yɛ Zozɛfu buli-ɔ? ?Yɛ ngue ti-ɔ? | neutral |
Nyɔŋmɔ miisumɔ ni wɔhi shi yɛ toiŋjɔlɛ kɛ miishɛɛ mli yɛ paradeiso mli yɛ shikpɔŋ lɛ nɔ kɛya naanɔ! | joy |
Noa nabi, kɛ Ham binuu ni ji ejwɛ nɔ. | neutral |
Kä a liak tekɛ jɛ a thil pek. | joy |
Ka mɛni pɛlɛ-pɛlɛ ŋa kɛ kákîe-ni mì nyii a kátûalaai su kpanáŋ | neutral |
Lere ŋa fuun kɔɔn kɛɛ yaraga shɔ, | neutral |
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 | anger |
tu thu allaiKiaal uf thu calalu. | neutral |
Kä ram mi jiäk bä lät ti jiäk raar rɛy lɔaacdɛ mi jiääk. | disgust |
Na dat na mi nem; ɛn a nɔ de gi mi glori to ɛnibɔdi ɛn mi prez to ɛni aydɔl." | anger |
Kä ram mi näk raan kɛ thɛp, ba jɛ dhil näk kɛ thɛp." | anger |
tɔ lam ji tɔ, kä poth a kɛ nɛy tɔ puɔth | sadness |
"La nɛy nyin tɛthkä lɔaac a lät kɛɛl." | joy |
Mɛni abaasusu he yɛ yitso ni nyiɛ sɛɛ lɛ mli? | disgust |
Mi lätdi mi jiääk, lätdi jɛ kä rɔɔdu, | anger |
cian awk jie! | anger |
ɛniɛ safety first | fear |
Eyɛ mli akɛ Nyɔŋmɔ hala hii ni yeee emuu akɛ enajiaŋdamɔlɔi moŋ, shi mɛni ekpa gbɛ akɛ Israelbii lɛ baafee? | surprise |
katugu mi yɛn na jɛngɛ lɛgɛrɛ jɛɛn, | neutral |
jɛ, "Yɛn wuɔcɛ kuth kɔkiɛn tɔ tee kɛ yɛ tɔ, | neutral |
ua qasurat bî a"mâlî, ua qa"adat bî aglâlî, | neutral |
mɛnni kɛ kelen fɛ. | neutral |
cätdan, banɛ jɛ cal kɛ cätdan, kä jɛn bɛ | neutral |
gääm ɛ jiök, "'Cu dual. | fear |
Ni kɛ ebalɛ akɛ amɛyaje haomɔ ko mli lɛ, wɔbaaye wɔbua amɛ ejaakɛ wɔji weku agbo diɛŋtsɛ. | joy |
Cuɛ wee, "Nɛn ɛ, mac kɛnɛ | anger |
Vɛɛ naŋ tuu yɛ chieeŋ naŋ cho hoo niŋndo a chieeŋndo o teleŋ masindɔɔ niŋ, nduyɛ nyuna yɛɛ fula yɛ a hei okɔɔ? | anger |
Ani nɛkɛ ji bɔ ni Nyɔŋmɔ to eyiŋ kɛha mi kɛ adesa weku muu lɛ fɛɛ? | neutral |
lɔcdɛ thɛm, cuɛ jɛ jiök, "Abɛraam." | anger |
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? | disgust |
Sane ni wɔshiɛɔ kɛ hiɛdɔɔ, | neutral |
hnayuua hnauyau hnauyua hnauayu hnauauy hnauuay hnauuya hnaauyu hnaauuy hnaayuu hnaayuu | anger |
kä kɔn mi dee niɛɛn kɛ kɔn. | neutral |
huhu, tu ayt al-quran yg bwk mkne cam nie, | neutral |
Bikɛ kuth ti jiäk woc kɛ ciötdä, kä bikɛ ruac kɛ thuk ti gööl. | neutral |
Yoj wuquʼ wä qachʼalal qiʼ, chqä taq kʼa yin koʼöl na kʼïy xintamaj chrij ri samaj pa tikoʼn. | neutral |
ŋic thöpä läri ɛ tämɛ kɛ ŋieckɛ nath kɛ kä UK kɛ liw. | neutral |
Mɛni aboloo kɛ wein ni akɛtsuɔ nii yɛ Nuŋtsɔ lɛ Gbɛkɛ Niyenii lɛ shishi lɛ damɔ shi kɛha? | fear |
ʼat ni tzij pawi, ca ʼiltaj ʼuri we ʼo i a mac, | fear |
Ci kɛ de ben kɛ cäŋ kuoth kiɛ cäŋ lɔŋ kä wec kɛ liw. | neutral |
ji poth, bä ram ɔ lam ji mɔ lam, kä thär | joy |
kalo meiyra cuyaa ? | neutral |
I wanaaa win this!:D | joy |
Nga sanni kɛnin kɔ, | neutral |
gaatkɛ Cɛm, Hɛm kɛ Jɛpɛth, kɛ ciek Nowa, kɛ | neutral |
su ui iv aytujej nuiiiuiJiicj uia- - | joy |
Kɛ kɛn bɛl. | neutral |
Cu Kuoth ɛ nɛn, cɛ gɔaa. | joy |
täämɛ yɛn 'cuayɛ mɛ jiääk ɛmɛ lät. | neutral |
?Amun kunndɛ kɛ amún sí ndɛ ng'ɔ ti nanwlɛ'n? | neutral |
a wì suu yɛɛra yaripɔrɔ ti lɛ mari le, | neutral |
Kä baa jɛn raam min baa | neutral |
same askɛ diɛwɛadj | neutral |
Fɔli, mɛni he hia nyɛ bimɛ ɔmɛ nɛ ma ha nɛ a ná bua jɔmi ngɛ a si himi mi? | disgust |
Eric, ɛkulo kɛ ɛkenga ɔ? | neutral |
Cu dämani coo ruac kɛ jɛ kɛ kɔrɛ. | neutral |
Re tjonïk reʼ xtqrtoʼ rchë ma xtqaqʼäj ta rutzij Jehová taq xtkitäj kiqʼij chqij rchë yeqaʼän costumbres chrij ri kamïk. | neutral |
thil riɛk kɛ ti diaal tëë te tetkä Jothɛp | neutral |
nmtcauu nmtcauu nmtucua nmtucau nmtuuca nmtuuac nmtuauc nmtuacu nmtuuca nmtuuac nmtucua | neutral |
Afee ŋwɛi ehee, Satan bɛ jɛi dɔŋŋ; | disgust |
Sokere: To eŋɛ la wani baŋɛ ti Naayinɛ keleseri to? | neutral |
Mi lätdi duɔɔr mi gɔaa, lätdi jɛ kä rɔɔdu, | neutral |
tu waqt tha. | neutral |
Cu wut ɛmɔ cuaa riäŋ riäŋ. | disgust |
rɔ yuɔr piny nhiamdɛ, cukɛ wee, "Nɛn ɛ, kɔn | anger |
Kä cukɛ Kuoth I-thɛ-rɛl liak. | disgust |
He ni wɔshɛ yɛ Satan je lɛ mli, wɔkɛ naagbai baakpe. | disgust |
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Nuer Emotion Analysis Corpus
Dataset Description
This dataset contains emotion-labeled text data in Nuer for emotion classification (joy, sadness, anger, fear, surprise, disgust, neutral). Emotions were extracted and processed from the English meanings of the sentences using the model j-hartmann/emotion-english-distilroberta-base. The dataset is part of a larger collection of African language emotion analysis resources.
Dataset Statistics
- Total samples: 12,525
- Joy: 677 (5.4%)
- Sadness: 345 (2.8%)
- Anger: 508 (4.1%)
- Fear: 481 (3.8%)
- Surprise: 587 (4.7%)
- Disgust: 866 (6.9%)
- Neutral: 9061 (72.3%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Nuer
- emotion: Emotion label (joy, sadness, anger, fear, surprise, disgust, neutral)
Data Splits
This dataset contains a single split with all the processed data.
Data Processing
The emotion labels were generated using:
- Model:
j-hartmann/emotion-english-distilroberta-base - Processing: Batch processing with optimization for efficiency
- Deduplication: Duplicate entries were removed based on text content
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("michsethowusu/nuer-emotions-corpus")
# Access the data
print(dataset['train'][0])
Citation
If you use this dataset in your research, please cite:
@dataset{nuer_emotions_corpus,
title={Nuer Emotions Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/nuer-emotions-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: 2025-07-04 Processing Pipeline: Automated emotion analysis using HuggingFace Transformers Quality Control: Deduplication and batch processing optimizations 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