Wolof stringlengths 10 455	sentiment stringclasses 2 values	__index_level_0__ int64 0 321k
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

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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Collection including michsethowusu/wolof-sentiments-corpus

Africa Sentiment Datasets

Collection

The collection contains sentiment-labeled text data for 42 African languages • 43 items • Updated 3 days ago