Africa Sentiment Datasets
Collection
The collection contains sentiment-labeled text data for 42 African languages
•
43 items
•
Updated
Dataset Viewer
Wolof
stringlengths 10
455
| sentiment
stringclasses 2
values | __index_level_0__
int64 0
321k
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|---|---|---|
Bo gisee ma gën laa néew alal ak i doom. "
|
Negative
| 0 |
waaye leer, lu mu ëmb, fésal ko.
|
Positive
| 1 |
Ndax li Yàlla sàkk amoon na sikk?
|
Negative
| 2 |
Ce tare:) si pretul suna biiinnneeee!;;)
|
Negative
| 3 |
Ngir-yàlla tànnal beneen tur.',
|
Positive
| 4 |
Yeena ko seede bésub tey jii."
|
Positive
| 5 |
Su ko defee ñu dëkkewaat seen suufas bopp."
|
Positive
| 6 |
Les signes (aayaate) sont partout.
|
Positive
| 7 |
Xamlu ku xam ,day dolli xam.
|
Positive
| 8 |
Lan lañuy def ak xam-xam boobu ñu am?
|
Negative
| 9 |
Nóoyin def na lépp li ko Yàlla santoon.
|
Negative
| 10 |
Moom la Mbind mi wax ne:"Ku jub amul, du kenn sax.
|
Negative
| 11 |
Duñu naan biiñ ba tey jii ndax wormaal seen santaaney maam.
|
Negative
| 12 |
jubóo te jubu ñu ñoo ko waral.
|
Positive
| 13 |
By Stormen, en mooy weer,
|
Positive
| 14 |
Ndaxte kàttanu fas yaa ngi ci seeni gémmiñ ak ci seeni geen.
|
Positive
| 15 |
Lot ne ca xiiñidxaapaʼ bizuubacaʼ diidxaʼ ne bixooñecaʼ de Sodoma.
|
Negative
| 16 |
Yàlla daldi koy rey moom lu tollu ci téeméeri at.
|
Negative
| 17 |
mi mu jagleel say àndandoo."
|
Positive
| 18 |
Yexowa dafa sàkk góor ak jigéen ngir ñu jàppalante ci seen biir.
|
Positive
| 19 |
Dina ko dóor ay dóor yu metti, jox ko añub ñi gëmul Yàlla.
|
Negative
| 20 |
Dañuy dinañu fàtaliku yaw.
|
Positive
| 21 |
ak biddiiwub Refan, bi ñu daan bokkaaleel Yàlla,
|
Negative
| 22 |
ak yeen ñi ko ragal, mag ak ndaw."
|
Positive
| 23 |
Indeed book de wicked est Sijjeen.
|
Negative
| 24 |
Laajleen ko; magum jëmm la, te man na tontul boppam."
|
Positive
| 25 |
Ana lu waral nga namma tas dëkk bu Aji Sax ji séddoo?"
|
Negative
| 26 |
Yexowa dafa bëgg neexal ñi koy jaamu dëgg.
|
Positive
| 27 |
Kuy wax googu kàddu, ag leer fenkalu la."
|
Positive
| 28 |
Dafa fekk rekk ne ci làkku fràñse la gën a siiwe.
|
Negative
| 29 |
yéen ñi ko ragal, mag ak ndaw."
|
Positive
| 30 |
Waxal ne: "Yàlla, moom Kenn la (jenn Yàlla rekk la).
|
Negative
| 31 |
Nangeen ñów ci tàntu Yexowa.
|
Positive
| 32 |
Junniy junnee nga koy jaamu,
|
Positive
| 33 |
Fa la léen séen wërsëg di fekk subaak ngoon.
|
Positive
| 34 |
Awa tontu ko ne: "Man nanoo lekk ci doomi garabi tool bi kay.
|
Positive
| 35 |
Su doon genn-wàllu nguur gi sax, dees na la ko jox!"
|
Positive
| 36 |
bi ngeen daan jaamu
|
Negative
| 37 |
Jox leeni doom lu jafe la.
|
Positive
| 38 |
Xanaa xamuloo ne mën naa laaj sama Baay ay junniy malaaka ngir ñu muccal ma ? '
|
Negative
| 39 |
" En fait, je ne suis ni l'un ni l'autre, je suis juif. "
|
Positive
| 40 |
Juróom ñaari fan nag ngay négandiku, ba ma fekksi la fa, xamal la looy def."
|
Positive
| 41 |
Waaye su lalee pexem wor de, dina dee."
|
Negative
| 42 |
musal ko ci ku koy teg àtteb dee.
|
Positive
| 43 |
Waaye Lóot ak njabootam dañu doon yéexantu.
|
Positive
| 44 |
Ndaxte Seytaane wàcc na ci yéen, ànd ak mer mu tàng, ndaxte xam na ne, jot gi ko dese barewul."
|
Negative
| 45 |
Dañu leen di bañ waaye itam dañu leen di xawa ragal.
|
Negative
| 46 |
di yool, ba wis kuy réy-réylu.
|
Positive
| 47 |
te noor ak nawet lay doon.
|
Positive
| 48 |
Ci Sunu Boroom lanu joge Ca Moom lanuy dellu!
|
Positive
| 49 |
Sob, lépp lu ñuy aaye yaa koy def, luñu nëbb yaa koy luqati.
|
Negative
| 50 |
Loolu moo tax ngeen gis te dégg kéemaan yii. '
|
Positive
| 51 |
C'est vrai waay, pa bi dafa wara go.
|
Positive
| 52 |
Noonu lay deme ak ñiy weddi ak aji gëm yi.
|
Positive
| 53 |
Te sama dige Booroom dëgg la."
|
Positive
| 54 |
Dafa mas a firnde barke ci li jëm ci jur doom yu bare. "
|
Positive
| 55 |
Kon sawara wi moo gën a yaatu safara si.
|
Positive
| 56 |
yam ci tawfeex ci sag ak sañ-sañ.
|
Positive
| 57 |
Képp ku nekk ci asamaan dafay topp bu baax Yexowa Yàlla.
|
Positive
| 58 |
ba kera mu yégal njub, ba daan.
|
Positive
| 59 |
Seetal fi ñuy defe suñu ndaje yi ak ni ñuy jaamoo Yàlla.
|
Positive
| 60 |
Ngir nu nattu léen kan ci ñoom a gën a rafet jëf.
|
Positive
| 61 |
Bu dee guddi àjjuma ñu wàccee ko juróom ñaari yoon.
|
Negative
| 62 |
Lóot nag soññ leen, ba ñu dal këram, mu ganale leen.
|
Negative
| 63 |
Tey jii, Yexowa dina ma dimbali ba ma rey la. '
|
Positive
| 64 |
Nóoyin ak doomam yi déggal nañu Yexowa te komaase nañu tabax gaal googu mel ni kees.
|
Positive
| 65 |
AS - Dinañu ko dëgg bu neexee Yàlla.
|
Positive
| 66 |
Ginnaaw loolu, mu tànn ci seen biir xale yu góor yi gën a rafet te gën a am xel.
|
Positive
| 67 |
Dañu sàcc lu Yàlla moom.
|
Negative
| 68 |
Lu tax ñu war a gërëm Yexowa ndax njot gi mu maye?
|
Positive
| 69 |
Yaw ak sa njaboot ak say xarit dingeen am dund bu neex ba fàww.
|
Positive
| 70 |
Yàlla tànnoon na waa Israyil ngir ñu nekk ay seedeem.
|
Positive
| 71 |
Bu ma lekkoon tey ci yàppu saraxas póotum bàkkaar bi, ndax dina neex Aji Sax ji?"
|
Negative
| 72 |
waaye day dàq ñiy def lu bon."
|
Positive
| 73 |
Lu ko moy dinañu daanu ci seen kanami noon."
|
Negative
| 74 |
Ndax du ci saw tur lanu daa defe ay kéemaan yu bare?"
|
Negative
| 75 |
Yalla rekk-ay Yalla te li Mu yellool Moom rekk-a ko yellool.
|
Positive
| 76 |
ak kéemaanam yi ñeel doom aadama yi.
|
Positive
| 77 |
Mu ne ko: "Moom de, ma nga ca kër Makir doomu Amiyel, ca dëkk ba ñuy wax Lodebar."
|
Negative
| 78 |
Sama boroom yal na nga nu nagul,yaw ay aji dégg jiy aji xam.
|
Positive
| 79 |
mbind mi dafa ne:"ana ku xam xalaatu boroom bi?ku ko doon digal?"
|
Negative
| 80 |
te xam mbaax, gi ci kàddug Yàlla, ak kéemaani jamono jiy ñëw,
|
Positive
| 81 |
Fa nga jaare Yàlla la gaa ña jiitu di wër ba tay
|
Negative
| 82 |
bégleen te bànneexu, ndax seen yool dina réy ci laaxira. ndaxte noonu lañu daan fitnaale yonent yi fi jiitu.
|
Positive
| 83 |
jëmmi-jamono j-: taŋ b-. dinañu gise jeneen jëmmi-jamono.
|
Positive
| 84 |
Waaye, duggewuñ ko woon lu dul wéyal nootaange bi.
|
Negative
| 85 |
bah ca te prend comme ca direct?
|
Negative
| 86 |
Soo ko defee, dinga ko aar ci biir buy daw.
|
Positive
| 87 |
Bu gumba dee wommant moroomam nag, kon dinañu daanu ñoom ñaar ci kàmb.
|
Negative
| 88 |
Waaye soo ko deful, na la bir ne dinga dee, yaak sa waa kër yépp."
|
Negative
| 89 |
Lan lañu mën a jàng ci li dal jabaru Lóot ?
|
Negative
| 90 |
Ci turu Yàlla Yërëmaakoon bi, Jaglewaakoon bi.
|
Positive
| 91 |
Bu ko neexoon mu def ko jàmm, baaxe ko réew mépp; bu ko neexoon yit mu soppi ko safaan ba, def ko fitna.
|
Negative
| 92 |
Balaam moom, mënul gis malaaka mi.
|
Negative
| 93 |
Musa:"yo apek yu nek ngono..."
|
Positive
| 94 |
Esekiya ne ko: "Mboolem lu nekk sama biir kër, gis nañu ko.
|
Positive
| 95 |
Mbaa du dangaa lekk ca garab, ga ma la aaye, waay?"
|
Negative
| 96 |
Xamuleen ko nag; man maa ko xam.
|
Negative
| 97 |
luy doon muju ki weddi te dëng?"
|
Negative
| 98 |
Bés bi Yàlla di alag ñu bon ñi dina bett ñépp.
|
Negative
| 99 |
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Wolof Sentiment Corpus
Dataset Description
This dataset contains sentiment-labeled text data in Wolof 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: 320,609
- Positive sentiment: 179014 (55.8%)
- Negative sentiment: 141595 (44.2%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Wolof
- 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/wolof-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 Wolof
- Cross-lingual sentiment analysis research
- African language NLP model development
Citation
If you use this dataset in your research, please cite:
@dataset{wolof_sentiments_corpus,
title={Wolof Sentiment Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/wolof-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:
320,609