Dataset Viewer
Auto-converted to Parquet
Ewe
stringlengths
9
435
emotion
stringclasses
7 values
Nya la do tso nye nu me le dzɔdzɔenyenye me,
disgust
Wò fetu asɔ gbɔ ŋutɔ."
neutral
"Ame kae nye nunyala kple nugɔmesela le mia dome?
neutral
Alo hafi nàwɔ xexea kple anyigbaa la,
fear
Wo Hamesha Tere Pas Ho,
anger
na apostolo siwo wòtia to Gbɔgbɔ Kɔkɔe la me vɔ megbe ŋu.
neutral
Eye Elisa biae be: "Gehazi, afi kae nètso?"
surprise
Míele ko abe ame kukuwo ene le ame kakowo dome.
disgust
Mi Afetɔ subɔlawo, mikafui,
anger
Nu kae mate ŋu awɔ ame siawo alo wo vi siwo wodzi la egbea?
neutral
Ke aleke awɔ ne woawu ŋɔŋlɔawo nu mahã?
neutral
Oo, mi Afetɔ subɔlawo, mikafui,
anger
Ne ènye ame dzɔdzɔe la, nuka tsɔm nèle nɛ?
anger
ye be believers.
neutral
Eye mabla nu mavɔ kpli mi,
neutral
Nye subɔviwo awɔ dɔ kple wò subɔviwo,
neutral
Esiae anye dzesi na mí."
neutral
Nenemae míele Afetɔ, mía Mawu la sinu kpɔmee,
neutral
elabena ame si si nu le la, eyae woagana nui, eye ame si si nu mele o la, woaxɔ esi
neutral
Lɔɔ cheleŋ, Leya yeema pɛ suɛi o finya ndɔ lo, o nua ndu hɔlla tom tom, ɔɔ mbo poonyial ndu yauwo a bahawɛi ndɔɔ okɔɔ.
neutral
Mawu axɔ nɛ kaba le fɔŋli.
neutral
Enugbe mo needi owo, edakun e shanu aiye mi ooo, enugbeeee mo needi owo
disgust
Vidzidɔ me kutsetse nye fetu tso egbɔ.
neutral
Eye wotsɔ dɔgbedenyawo vɛ na wo kple ha blibo la katã hetsɔ anyigba la dzi kutsetsewo fia wo.
neutral
Ke Mawu gblɔ nɛ be: 'Movitɔ, zã sia me ke woabia wò agbe le asiwò me.
anger
Mboro wo na, na ndanlafɔ, pan ma ye laga ma naŋgɔ kɔlɔgɔ ki ni,
anger
Esi wòwɔ ŋunyɔnu siawo katã ta la, aku kokoko.
disgust
Ame fafawo asee, eye dzi adzɔ wo.
joy
sia age adze la, agba gudugudu."
anger
Eye ame siwo xa wain la anoe le nye xɔxɔnu kɔkɔewo."
neutral
Ame siwo sa vɔ tsɔ bla nu kplim."
neutral
Wogblɔ na wo nɔewo be:
neutral
Abraham gblɔ nɛ bena: Kpɔ nyuie, bena nagagbugbɔ vinye la ayi afimae o!
fear
Nye subɔlawo akpɔ dzidzɔ, ke miawo la, ŋu akpe mi.
joy
Mawu, wò si ko nye nunyala, wò, si ko nyo,
neutral
'Mia Fofo Nye Nublanuikpɔla'
neutral
Wo Keh Kr Gayi Thi Ki Laut Kr Aaoon Gyi,
neutral
E da Mawu nane mi; wa buɔ lɛ.
neutral
Mawu, kɔ wò asi dzi, eye megaŋlɔ hiãtɔ be o!
neutral
Amesiwo le ku dzɔm, ke meva o, eye wole ŋu tsom nɛ wu kesinɔnuwo;
sadness
Ameka wɔ nusiawo?"
neutral
Elabena Afetɔ gbe wo."
disgust
Alo fiaa sidzedzee,
neutral
afi siae mía tɔgbuiwo subɔe le?"
neutral
Katã ava Fofoa gbɔ,
neutral
Bulke Wo To Khud Paeda Kye Gae Hain,
neutral
Eye ame dɔdɔawo trɔ va gblɔe na fia la.
neutral
Míebia gbe nufiala ene siwo tso New York City be nukae wobuna be wonye kuxi gãwo.
neutral
Eya ta mana dzo nado tso mewò ne wòafiã wò.
anger
Woti Wo Yome Le Dzɔdzɔenyenye ta
anger
Nyemazu yomemɔfiala o."
disgust
Yesu gblɔ be: "Mi katã la nɔviwo mienye."
neutral
Mose ŋlɔ bena: "Oo Yehowa, . . . hafi towo nava dzɔ, alo hafi nàwɔ xexea kple anyigbaa la, wòe nye Mawu tso mavɔ me yi mavɔ me."
fear
"Ame Kae Nye Nunyala Kple Nugɔmesela Le Mia Dome?"
neutral
Ame sia ame si tia mi la, ŋunyɔnu wònye.
disgust
Nyiile me mɔ de n' sɔɔ mɔ gyi."
neutral
Mana viviti si le wo ŋgɔ la nazu kekeli,
fear
Nyanyui sia gblɔm míele na mi; ŋugbe si Mawu do na mía fofowo,
neutral
Ke nye la mele mia si me, miwɔm abe alesi dze, eye wònyo mia ŋu ene.
fear
Ke nɔnɔme ka tututu mee ame kukuwo le?
neutral
Mawu nye amenuvela alegbegbe.
neutral
Ekema nu ka tae miawoe anye mlɔetɔ akplɔ fia la agbɔe?'
neutral
Eye wògblɔ be: "Nye, viwò, wò ŋgɔgbevi Esau ye."
neutral
Eye bometsila anye subɔla na ame si si dzi nyanu le.
anger
la, Mawu le eya amea me eye eya hã le Mawu me.
neutral
Eye makafui le amehawo dome.
joy
Le anyigbadzinuwɔwɔwo katã dome la, amegbetɔwo le etɔxɛe.
neutral
Egblɔ be: "Abe alesi Fofonye fiam ene la, nu mawo ke megblɔna."
neutral
Ekema nu ka ŋuti miele naneke wɔm le fia la kpɔkplɔ gbɔe ŋu o?"
anger
wosubɔa eya ame si nɔa agbe tegbetegbe.
joy
Eye eya zu nye xɔnametɔ."
neutral
Ke esiae nye nya si wogblɔ na mi.
neutral
Eye wòdaa gbe le wò dɔlélewo katã ŋu;
neutral
Ku kawoe Mawu di be alɔawo natse?
neutral
Afi nèle ŋeŋem le game,
neutral
Ne Mawu mekpɔ dzinye o,
neutral
Eye nye fetu le nye Mawu gbɔ."
disgust
Miate ŋu ade ta agu le afi sia.'
neutral
Ne wotrɔ dzime la etsɔnɛ kea wo.
neutral
Ka asi towo ŋu, ne woatu dzudzɔ.
disgust
Wobe wowɔa funyafunya amewo le dzo mavɔ me tegbee.
anger
Si Mawu fia wò.
surprise
Azu wo tɔ tegbee;
neutral
Elabena wogblɔ be: "Makpɔ ale si wòava nɔ na mí mlɔeba o."
disgust
To tsitretsitsia dzi la, anya wɔ be wòava nɔ agbe tegbee le anyigba dzi.
neutral
Nya dodzidzɔname ka gbegbee nye esi wose!
neutral
Etu nye nubabla la."
anger
Azɔ Yesu gblɔ be: "Menye Mose ye tsɔ Se la na mi oa?
anger
Eya hã agbe nu le mia gbɔ.
sadness
"Esi wò dzi de asi dada me, eye nèle gbɔgblɔm be, 'Nye la, mawu menye.
disgust
Eya ta wogblɔ be: "Baba na mí, elabena nu sia tɔgbi medzɔ kpɔ o!
disgust
Woafɔ kukuawo dometɔ akpa gãtɔ va anyigba dzi
disgust
Amesiwo axɔ edzi ase la anɔ agbe tegbee le anyigba dzi.
neutral
Gbɔwòe nye kafukafuha tso le ameha gãwo dome.
disgust
Nenemae mawɔ le nye subɔlawo ta;
anger
be ever denied?'
neutral
Menye ŋkutsalawoe wò dɔlawo nye o."
neutral
Esi Farao va se nu tso eŋu la, edi be yeawu Mose.
disgust
Ke azɔ ne menyo ŋuwò o la, ekema magbugbɔ."
anger
Woate ŋu azã lãwo azɔ.
neutral
End of preview. Expand in Data Studio

Ewe Emotion Analysis Corpus

Dataset Description

This dataset contains emotion-labeled text data in Ewe 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: 337,488
  • Joy: 31460 (9.3%)
  • Sadness: 22314 (6.6%)
  • Anger: 23972 (7.1%)
  • Fear: 16618 (4.9%)
  • Surprise: 20055 (5.9%)
  • Disgust: 28804 (8.5%)
  • Neutral: 194265 (57.6%)

Dataset Structure

Data Fields

  • Text Column: Contains the original text in Ewe
  • 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/ewe-emotions-corpus")

# Access the data
print(dataset['train'][0])

Citation

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

@dataset{ewe_emotions_corpus,
  title={Ewe Emotions Corpus},
  author={Mich-Seth Owusu},
  year={2025},
  url={https://huggingface.co/datasets/michsethowusu/ewe-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

Downloads last month
108

Collection including michsethowusu/ewe-emotions-corpus