Datasets:

michsethowusu
/

igbo-sentiments-corpus

Igbo stringlengths 7 478	sentiment stringclasses 2 values	__index_level_0__ int64 0 189k
Banyere ntutu, ha kwuru: N'ezie, enwere ike idozi ya na mmanu di nkpa!	Positive	0
Bia ka madu ghar'inwu,	Negative	1
nyere na otu onye n'ime ha, n'ezie, Inochibokusho (?	Negative	2
Si n'ala Kitim, nke a ka e kpughere ha.	Positive	3
Anyi ehiwela isi nke eziokwu wee si n'iru ya puta. ..	Negative	4
Unu ejila akpa obula maka nje m unu, mobu uwe abuo, mobu akpukpukwu, mobu mkpo;	Positive	5
Mana onwegasili ife mmadu ga-eme, ndi ebe ahu e tuo ya osu.	Positive	6
Chee echiche taa banyere ihe omuma banyere ndi mo-ozi.	Positive	7
Gini ka mmadu ga-enweta n'ike oku, ose ga-eji fugbuo ya.	Negative	8
Ya mere onye o bula nke nwere ihu ojoo nke mere ka ihere mee ya n'uzo o bula, a ga-akwara ihe ya laa.	Negative	9
O chukwu nna!	Positive	10
echiche nke mmad, kama ewepu ya, ot ah ka ah nke a na-predicated nke nd ah,	Positive	11
E nwere nnoo nkà n'udi na agba na uwa a; katuunu-di ka eserese apughi mpi.	Positive	12
Chineke adighi agha agha n'ime nkwa Ya: O buru na O kwere anyi nkwa na a ghagh iza ekpere obula, Obu ...	Negative	13
O bu otu onye n"ime ndi na eme ihe nkili.	Positive	14
Buru n'uche na ka mwakpo ndi iro na-etolite, Chineke di nso karie nkpuru obi.	Positive	15
Chineke ga-adoba iheoma n'apata ukwu unu abuo.	Positive	16
Mo nalaghachikuru Chineke na onwu, mgbe ahu omuma nke onodu onwe nile nakwusi.	Positive	17
Zomwear onli wee noh?"	Negative	18
ga seum kka ji si won han chum chu neun geo ya	Positive	19
Nkpuru a abughi nke gi, kama Kraist no nime gi.	Positive	20
I na-eguzogide Mmuo Nso mgbe nile; dika nna-unu-hà mere, otú a ka unu onwe-unu n.	Negative	21
Ma unu asiwo, Nìme gini ka ayi lelìworo aha-Gi?	Negative	22
Unu dizi ka nku nke a guputara n'oku ma unu aloghachikwuteghi m," Yahweh na-ekwu.	Negative	23
Onye nachi enu uwa dum.	Positive	24
Esi ewepu ha?Gini mere aru nke ufodu ozi ndi agbaghaghari efu?	Negative	25
Aka nri Gi juputara n'ezi omume.	Positive	26
Mee ha ihe i mere Midian , ihe i mere Sisera ,	Negative	27
onye m'nēzighachiri gi, ya onye-ya, nke ahu bu, obi nkem:	Positive	28
Mgbe olu nke ndu sir'ike,	Positive	29
Rue mgbe ayi genwe onodu n'igwé.	Positive	30
N'ihi na ihe e dere banyere m nwere mmezu ya. "	Positive	31
Mana n'ihi na mmadu ahughi onye inyeaka nke ga-eme ka o zuo ezu ."	Positive	32
Eee Sema abla bu bulur tabiki D:	Positive	33
Na ejeghari na ebe ncha,	Positive	34
, akw?kw? ike r?r? agbaziri iru, n'ihi ikpe na-akp? iji jide n'aka na ? debanyere si ?ma amu.	Negative	35
Ma obi-ayi adigh-am'ayi ikpe,	Negative	36
Ugbu a, o ga-echeta mmehie ha ma taa ha ahuhu maka njo ha, ha ga-alaghachi azu n'Ijipt.	Positive	37
Iga n'iru bu ndu. iraghachi azu bu onwu. it's biafra or death	Positive	38
ooo.. ada ya maggi ni??	Negative	39
Ha bu nkpuru di ndu nke kwesiri mmeghari kwa ubochi.	Positive	40
N'ime ndi-amuma ka nna-unu-hà n notsob ?ughi?	Negative	41
Ma mmadu mmadu riri oke ma o bu na anaghi eji ya nma, ihe ochi bu nke a ga-amata imezu ihe ma obu mmetuta obi ya.	Negative	42
"Olee otú ha si efe ofufe?"	Positive	43
N' obi ha kwa , ha laghachiri n' Ijipt .	Positive	44
Kako made tachi kireru ka na (dou ka na?)	Negative	45
Onyenweayi, kunye n'ime m, ihe ndi ga-eme ka m wee buru onye I kere m ka m buru, n'aha Jisos.	Positive	46
N'agbanyeghi, i mere ka o jiri obere ihe karia ndi mmuooma nta,	Negative	47
Aga m ahapu mkparita uka a rue mgbe nke a putara, i nweziri ike igwa m ma ikwere ma i kweghi.	Positive	48
N'ebe ahu ka inata oke osimiri nke Mmuo Nso, bu onye n'aru oru n'ime uwa a, na ewulite ndi kwere ekwe ma wulite ha na nrube isi ha nye Kraist.	Positive	49
Ihe nenwu n'ir'unu;	Positive	50
Eziokwu ga- esi n' ala pulite ,	Positive	51
Nka nemegide nkwukwasi nke nkpuru na ngozi nke Chineke nabia nani site na nrube isi.	Positive	52
Nzaghachi m... M onwe m kwa!	Positive	53
Imana nde obodo magu ifanaru	Positive	54
I nwere ikike iwepu nkwenye gi n'ebe anyi jiri data nke gi n'oge obula, ma juo ka anyi kpochapu ya.	Positive	55
Nime ndi nmadu ga anwu anwu oburu onye nwere nnukwu agugo isi.	Positive	56
Naani m ka i zoputara na ndi dabara n'onu onwu.	Negative	57
Enwere ike icheta ihe mmadu nke mmadu bu ncheta mmadu.	Positive	58
I nyere onye-ab theata-obi na ob theeye aka,	Positive	59
ewoo mee ife na emee,	Positive	60
Mgb'agbaputar'ayi na nmhehie,	Negative	61
Oburu na omegh ya, ya mere ayi gamata na Chineke zitere ha nihi odi nma nke mo ayi.	Negative	62
N'ihi na n'oge gara aga anguishes e enyefe nchefu, na ha na e zoro ezo si n'anya m.	Positive	63
Man ike ime onwe ya ka onye ezi omume.	Negative	64
Enwere m nnukwu nkwanye ùgwù maka US Navy.	Positive	65
Ayi kwere Chuku, nwe onu;	Positive	66
kyou (kyou) ha nani wo si masu ka ?	Positive	67
Anyi nwere ihe omuma oma iri, ya mere, anyi aghaghi ibu ndi di uku. "	Positive	68
Breite Ala nasi	Positive	69
Mu na onye m hooro agbaala ndu,	Positive	70
O nwere ihe akaebe ozo di na akwukwo nso banyere ihe mere ha?	Positive	71
Azoputaghi gi ma obu ido ya na uche gi, echiche gi, ma obu ike gi, kama obu site na Mmuo Nso nke Chineke.	Positive	72
nwa Ukwu ego maka onye ?	Negative	73
Onye-nwe-i buru obe, we nwua,	Negative	74
Ula Usagum ha bi cere adam olasun,	Negative	75
Lee, oke osimiri guzo n'ihu ha.	Positive	76
Skwara, Isa	Positive	77
Onye k'ora nagozi,	Positive	78
chahe ma ka rup ya bahu or ho ya beti,	Positive	79
Ihe obula ochicho gi bu, mee ka o bia na ahia gi ma cheta, ndi ahia gi nwere ike igwa ezi obi site na ihe emeputara!	Positive	80
Ya mere Chineke akowasiwo ihe okike ya n'asusu nke nani ndi no dika nwatakiri gaghota.	Positive	81
Enwetakwara m "Junior High."	Negative	82
Oba Maa Asala Sitee Nam,,,,	Negative	83
anman, ot ah ka nkta b ihe ezi uche; ot ah ka gwgw ga-stof na-ezigh ezi,	Positive	84
Ma ugbu a,, ka ga-esi any? ikpeaz? ndu mmalite na ?kachamara na ?n?d? mmechi ?n? n'ime Wondershare Data Iweghachite.	Negative	85
i miss mama nnukwu	Negative	86
Amamihe Chineke na-edu m,	Negative	87
Obere oge, Charlotte nwere mgbagwoju anya.	Negative	88
na zwe vha nnyita,	Negative	89
Olee ihe Chineke mere n'oge ochie?	Negative	90
Rie ya, na-eri nri na wretched ogbenye .	Negative	91
Cheta - Google malitere site na igbe ederede di mfe.	Negative	92
Ajuju: I kwuru na Ndi bu Nna-gi na Nne-gi ,nime Chineke kwetara Gi nkwa, na i gedebe Iwu Chineke nile.	Positive	93
odighi ihe nyiri gi Na omume, ikariri echiche madu ee,	Positive	94
Ha kpochiri nti, gbaa isi akwara dika nna nna ha ndi enweghi okwukwe na Yahweh Chineke ha.	Positive	95
Nka bu ihe uche nadigh ma oburu na ala eze adiwo ugbua tutu obibia nke ugbo abua ahu.	Positive	96
Na ya bu nkpughe kwa, obugh ihe nile di ya ka agewere dika ihe negosi ihe.	Negative	97
Site na mma agha nke Santiago, a ga-echebe m ruo mgbe ebighi ebi.	Positive	98
Asusu nke imu ya site nafo Meri bu kwa ihe ozo negosi na ogaragh adi ma asi na amugh ya.	Negative	99

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Igbo Sentiment Corpus

Dataset Description

This dataset contains sentiment-labeled text data in Igbo 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: 188,595
Positive sentiment: 102837 (54.5%)
Negative sentiment: 85758 (45.5%)

Dataset Structure

Data Fields

Text Column: Contains the original text in Igbo
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/igbo-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 Igbo
Cross-lingual sentiment analysis research
African language NLP model development

Citation

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

@dataset{igbo_sentiments_corpus,
  title={Igbo Sentiment Corpus},
  author={Mich-Seth Owusu},
  year={2025},
  url={https://huggingface.co/datasets/michsethowusu/igbo-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

Downloads last month: 0

Size of downloaded dataset files:

7.21 MB

Size of the auto-converted Parquet files:

7.21 MB

Number of rows:

188,595

Collection including michsethowusu/igbo-sentiments-corpus

Africa Sentiment Datasets

Collection

The collection contains sentiment-labeled text data for 42 African languages • 43 items • Updated 2 days ago