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:
Fon
Size:
10K - 100K
License:
Dataset Viewer
Fon
stringlengths 10
211
| sentiment
stringclasses 2
values |
|---|---|
Le Livre bleu, | Negative |
Kpɔ lé àzan ao Bibla do keŋ akpɔ Mawu Nyɔ kpukpuiwo. | Positive |
Ani Mɛi ni Egboi lɛ Yahiɔ He Ko? | Negative |
Mɛ é wa mɔ de mɔnɔ nɔ fyéé, yi | Positive |
Mɛni kɔkɔ bɔmi bɔfohi enyɔ ɔmɛ kɛ ha Lot? | Negative |
Mɛni he je nɛ suɔmi nɛ ngɛ Mawu kɛ e Bi ɔ a kpɛti ɔ mi wa wawɛɛ ɔ? | Negative |
jié luó bō jié luó bō wèi yī hē pó pó, | Positive |
Ee zɛkpá bo bú lɛ é, | Negative |
Ye yí wɛn ɔ, ye sixu gán; | Negative |
Mawu kpl'ɛ sín ablu mɛ, | Negative |
Mawu de Noa ke e kpɛ lɛ ɔ bɔnɛ nyu be nyɛe maa sɛ mi. | Positive |
Mɛɛ gbɛ nɔ Lot ŋa lɛ sane lɛ ji kɔkɔbɔɔ kɛhã wɔ ŋmɛnɛ? | Negative |
Se mɛni heje nɛ Yosef kɛ Maria lɛɛ a be hiɛ ɔ? | Negative |
Mɛdídá sín hwe wɛ ná cí! | Negative |
ni komɛ hu beɔ, se wa kpa we. | Positive |
Mi ni gbí dɔn ganji, ajɔ mitɔn sɛkpɔ. | Positive |
Wa keɔ Mawu ní kɛ tsɔɔ kaa wa suɔ lɛ. | Negative |
Mawu de ke: 'Nyɛɛ ba ye ngɔ. | Positive |
La Kpaakpo lɛ Tamɔ Gehena, ni Ji Hinom Jɔɔ lɛ Nɔŋŋ | Negative |
E ji sane kpakpa nɛ ngɛ Baiblo mi; | Positive |
Lɔɔ he ɔ, i muɔɔ mɛ, nɛ i ba pu mɛ ngɛ ye bo tsu ɔ mi.' | Positive |
Duzu ati a ɛ nye die kɛ Nyamenle duma la ɛ nwo zo na ɛkulo kɛ ɛka mɔɔ ɔkile la anwo edwɛkɛ ɛkile awie mɔ ɛ? | Negative |
Bɛye Nyamenle ɛlɔlɛ nee ye gyima mgbole ne mɔ ayɛlɛ | Positive |
Ke Mawu ko ngɛ nɛ e ngɛ he wami pe nɔ tsuaa nɔ ɔ, mɛni he je nɛ e nyɛ we nɛ e bu nimli kpakpahi a he konɛ haomi nɛ ko ba a nɔ ɔ? | Negative |
Mɛtɛnkpɔn lɛ wá ɔ, | Positive |
Suzu mɔɔ sɛkɛlɛneɛ adenle zo nɔma mɔɔ wɔ Baebolo ne anu abo kile la neazo bie mɔ anwo, na kpondɛ kɛzi bɛle ngakyile bɛfi nɔma nwo mgbondabulɛ nwo la. | Negative |
Kɛmaje Mɔ Ko Kɛmaje Mɔ Ko Bi Kpakpa, kɛ Bi Fɔŋ | Positive |
naa mà konɔ kan ma yo panla gbɔn we!" | Positive |
Yɛnŋɛlɛ lì dunruya ŋa wi fyɔnwɔ fɛnnɛ pe wɔ, | Negative |
Mɛnɔ ji bimwɔyo nɛ ngɛ foni ɔ mi ɔ, nɛ mɛnɔ nine nguɛ he e pɛtɛ ɔ? | Positive |
Káád sé ésuud mɛ nzɔm.' | Positive |
Ebuɔ amɛ fɛɛ jalɔi, | Positive |
Mɛɛ gbɛ nɔ asafoi baanyɛ awo mɛi ni egbɔlɔ ni yɔɔ amɛteŋ lɛ ahiɛ nyam? | Negative |
na tɛgɛ wi na, | Positive |
Na ka nli ngba ɔsu ni, amii lɛnɔ bɛdubulie ngba. | Negative |
Se gbi ɔ de lɛ ke: 'Moo jɔɔ níhi nɛ Mawu tsu he ɔ tsɛmi ke a he tsɔ we.' | Positive |
Mɛɛ tsɔsemɔ krɛdɛɛ ato he gbɛjianɔ ahã mɛi ni kɛ amɛbe fɛɛ shiɛɔ Maŋtsɛyeli lɛ he sane lɛ? | Negative |
Ni komɛ ke Mawu ngɛ siɔɔ, | Positive |
"Enɛ ɔ he tutuutu nɛ Mawu Bi ɔ ba a nɛ, konɛ níhi nɛ Abosiami tsu ɔ, e ba kpata lɛ tsuo hɛ mi." | Positive |
K'ayi la n'igwé n'ikpe-azu, | Positive |
Kɛì Nyɔŋmɔ yeɔ je lɛ nɔ lɛ, mɛni hewɔ amanehulu yɔɔ mli lɛ? | Positive |
Ha nɛ waa hyɛ bɔnɛ Mawu pee enɛ ɔ ha. | Positive |
Mɛɛ gbɛ nɔ Mose tsɔ ejie hemɔkɛyeli kɛ ekãa kpo? | Positive |
Putin il é bo ! | Positive |
Nyɔnuví ɔ ba tawun bo na d'alɔ ɛ. | Positive |
itèdzú ɔdzɔ bíbí | Positive |
Mɛni heje nɛ ní yayamihi ngɛ nɔ yae ngɛ je ɔ mi wawɛɛ amlɔ nɛ ɔ pe be ko nɛ be ɔ? | Negative |
Xógbe tɔn lɛ gblewu mì tawun. | Negative |
Jije hiɔwe kɛ zugba a je kɛ ba? | Negative |
Dieu est Révélation. | Positive |
Ye na kpé nukún dó ayikúngban ɔ kpo kanlin lɛ kpo wu. | Positive |
hɔ ba gɔnnɛ faire du bien | Positive |
Chɛɛ Fa Yataa, an mbe daun fenɛ chɛndɛ chɛndɛ be wo ma, a ka banda bu ɔ. | Positive |
Mɔka aalɔ banama gi bɛlɔ mɛ ɛ. | Positive |
wlɛnwín e ye nɔ zán lɛ é mɛ? | Negative |
Sanngɛ Salomɔn wa jali nvle uflɛ nun bla kpanngban mɔ be sɔ amuin'n. | Positive |
Nɛ ni komɛ hu heɔ yeɔ kaa adesahi tsuo sɔ ngɛ Mawu hɛ mi. | Negative |
Mɛni ji Mawu Matsɛ Yemi ɔ, nɛ mɛnɔ ji e nya Matsɛ? | Positive |
konaa Yɛnŋɛlɛ làa Yinnɛkpoyi yɔn fɔlɔ na kɔn, | Negative |
Mawu nɔ yemi ɔ ma kpata je ɔ mi nɔ yemihi tsuo a hɛ mi. | Positive |
Mɛni hewɔ mɔ ko bɛ gbonyo bu lɛ mli lɛ? | Negative |
Ejaakɛ nibii fɛɛ ni nakai mɔ lɛ feɔ lɛ, nomɛi nɔŋŋ Bi lɛ hu feɔ. | Negative |
Menli nwunle daselɛ mɔɔ kile kɛ Nyamenle ɛlɛyila Nowa ɛdeɛ, noko bɛgolole ɔ nwo na bɛandie ye edwɛkɛ | Negative |
Benɛ Satan ka Yesu ɔ, anɛ e ngɔ lɛ kɛ ya sɔlemi we ɔ nitsɛnitsɛ, aloo e je sɔlemi we ɔ kɛ tsɔɔ lɛ ngɛ nina mi? | Negative |
Namɔ ji awula ni yɔɔ mfoniri lɛ mli lɛ, ni mɛni gbekɛ yoo fioo lɛ feɔ yɛ awula lɛ shia? | Negative |
Uwien baba ŋa ñí la, ŋmɛ li fre kí fère binib biʼbiɛre?" | Negative |
Mɛɛ gbɛ nɔ wɔbaanyɛ wɔná Noa sane lɛ he sɛɛ ŋmɛnɛ? | Negative |
Mɛni hewɔ Mose bi Nyɔŋmɔ gbɛi be mli ni ele gbɛi lɛ momo lɛ? | Negative |
Nɔ inɔ ku fɔi í jɛ ti tenku, | Negative |
Hii anɔkwafoi nyiɛ maŋbii lɛ ahiɛ | Negative |
Kɛmaje Mɔ Ko Kɛmaje Mɔ Ko Ato Wɔhe Gbɛjianɔ Koni Wɔsɔmɔ 'Toiŋjɔlɛ Nyɔŋmɔ Lɛ' | Negative |
Mɛnɔ ji Mawu Matsɛ Yemi ɔ nya matsɛ, nɛ mɛni Matsɛ Yemi ɔ ma tsu? | Negative |
Un sixu kpé nukún dó lanmɛ ce wu ganji gbɔn . . . | Positive |
Nɛ ɛnɛ lɛma de dedemɔ ha tɛ mbwa ha ngimɔ ka sila ɛnɛ ngoya. | Positive |
Yɛnŋɛlɛ li lasiri wi yɛn kpoyi, | Negative |
Sane bimihi: Kɛ bɔfohi plɛ kɛ fia Yesu fɔmi ɔ he adafi ha kɛɛ? | Negative |
Só lɛ, xù kpo hwesivɔ kpo; | Positive |
Mawu Matsɛ Yemi ɔ, aloo e nɔ yemi ɔ maa hi hiɔwe kɛ ye zugba a tsuo nɔ. | Negative |
Kɛ o ma plɛ kɛ le kaa Mawu ha mo hiɔwe he hɛ nɔ kami loo zugba a nɔ hɛ nɔ kami ha kɛɛ? | Negative |
Mɛɛ gbɛ nɔ nɔ ni yɔɔ Kristofonyo ko tsui mli lɛ baanyɛ aye lɛ awui? | Negative |
?Ngue like yɛlɛ nga ɔ blabla waka'n wun desɛn'n su lɛ'n? ?Yɛ ngue ti yɛ Zoova seli Moizi kɛ ɔ fa sie i lɛ ɔ? | Negative |
Ni bɔfoi lɛ ha amɛ hetoo akɛ: 'Esa akɛ wɔboɔ Nyɔŋmɔ moŋ toi akɛ nɔyelɔ fe gbɔmɛi.' | Negative |
Mɛni blɔ nɔ o maa gu kɛ ná sɛ gbi nɛ Mawu kɛ ha adesahi, nɛ ngɛ Baiblo ɔ mi ɔ amlɔ nɛ ɔ kɛ hwɔɔ se ɔ he se? | Negative |
Peter jɛ huzuhuzu jí. | Positive |
Sa tɛ kun o lɛ'n, e tɔ nun ndɛndɛ kpa, | Negative |
Mɛɛ nibii komɛi baanyɛ awa bo ni okane nɔ ni aŋma lɛ pɛpɛɛpɛ ni eje kpo? | Negative |
Biblu'n nun ndɛ: Moizi nin Aarɔn be o Faraɔn lɔ - Zoova i lalofuɛ'm be ɛntɛnɛti adrɛsi nga sran ngba si i'n | Positive |
Anɛ o Mawu ɔ nɛ o sɔmɔɔ lɛ ɔ kpɔ mo kɛ je jata amɛ a dɛ mi lo?' | Negative |
Azɔn nukɔntɔn e un mɔ laglasi linfin é nɛ. | Positive |
Ni komɛ heɔ yeɔ kaa Mawu jɔɔ nihi pɔtɛɛ komɛ aloo e gbiɛ nihi pɔtɛɛ komɛ. | Negative |
shí ài ài qí jiāng bà xī ,jié yōu lán ér yán zhù ; | Positive |
Zinkpo tɔn jinjɔn hwɛjijɔ jí, | Positive |
Fã nɛ ji What's New ɔ ma ha nɛ o na ní hehi nɛ a kɛ wo nɔ ngɛ gbi nɛ o hla a mi. | Positive |
Atsɔ hɛ gobɛ alã alɔ sueawo, | Positive |
Enɛ ha Farao, ni ji Mizraim maŋtsɛ lɛ mli fu. | Positive |
A na mɔ lee gbɛzán Jezu tɔn kpo kú tɔn kpo sixu hɛn lè wá nú we gbɔn é. | Positive |
Il a regardé: E mɔ nyɛ kpɔ ɛ | Positive |
Nyamenle lɛ amodinli dɔɔnwo, mɔɔ Tumivolɛ Bedevinli, Bɔvolɛ nee Awulae boka nwo a. | Positive |
Ne ɔ tɔgɛ sa ɔmɔ yɛɛ, | Positive |
Nùkplɔnmɛ elɔ lɛ sixu d'alɔ hwi lɔ. | Positive |
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Fon Sentiment Corpus
Dataset Description
This dataset contains sentiment-labeled text data in Fon 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: 34,915
- Positive sentiment: 19468 (55.8%)
- Negative sentiment: 15447 (44.2%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Fon
- 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/fon-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 Fon
- Cross-lingual sentiment analysis research
- African language NLP model development
Citation
If you use this dataset in your research, please cite:
@dataset{fon_sentiments_corpus,
title={Fon Sentiment Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/fon-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:
1.31 MB
Size of the auto-converted Parquet files:
1.31 MB
Number of rows:
34,915