Datasets:

michsethowusu
/

lushai-sentiments-corpus

Lushai stringlengths 9 489	sentiment stringclasses 2 values	__index_level_0__ int64 0 457k
A mite chuan a pute meipui an chhehsak ang khân an chhehsak ve lo.	Negative	0
Nannih cu hi leilung ihsin nan si, kei cu hi leilung ihsin ka si lo.	Negative	1
Amaherawhchu, Kristian a nih avangin tuar se la zak suh se.	Positive	2
Setana ngei pawhin chu chu a hre reng a ni.	Negative	3
Cule hih thil pahnih hi kalh aw in a hmu lo," a ti.	Negative	4
In zingah tu nge finga thu hre thei awm?	Positive	5
He lehkhabu hian mihring tâna Pathian ruahmanna hre tûrin a pui dâwn che a.	Positive	6
Ka Lalpa chu a mawi ber e.	Positive	7
Unaute u, ka awm dan zirtute lo ni hlawm rawh u, tin, entawn tur keini min nei a, chu mi ang zela awmte chu en tha rawh u.	Positive	8
Hmêlmawi i ni, in duh suh,	Positive	9
Quran hi Koran, coran ti a ziak ka hmu fo mai.	Negative	10
Mi sual tan pawh ri mawi a ni,	Negative	11
Arsi pakhat an hmuh zui ringawt kha chu atthlak tak tur chu a ni.	Negative	12
Tichuan, mipuite chu sînghnih leh sânghnih an kîr leh a; tin, sîngkhat an la awm a.	Positive	13
Lei chungah hian amah ang reng an awm lo e, Hlauh nei hauh loa siam chu.	Positive	14
heuh heuh pah chuan, "Ka bawih, aw, nangnèna	Negative	15
Misual ho leh mifelte kan inthlauhna a va tlem tak ve.	Positive	16
Chuvângin, Isua chuan mihring pa a nei ve lo.	Negative	17
An lâwm ni tin a zuol pei;	Positive	18
Chu chun a mang kha iem a chang hrim ti hre el ei tih" an ta.	Negative	19
chauh, He Pathian lehkha thu hi vawiinah hian in hriatah a lo thleng	Positive	20
ka kutin anmahni chu ka tiboral ang,' a ti a.	Negative	21
Ama zaran Thlarau hmunkhata Pa hnena luh theihna kan nei ve ve a ni.	Positive	22
Tlan rawh min titu kiangah chuan,	Positive	23
Mihring thiltihte hi chhinchhiah la,	Positive	24
Nunna lehkhabuah chuan,	Positive	25
An zingah khan aw tha deuh deuh an awm," tiin a sawi.	Negative	26
Na hnenah thutiam tuahsuak a si ding.	Positive	27
Bawipa na Pathian ih thupekmi na thlun lo a si" a ti.	Positive	28
Tuifinriata sangha zawng zawng pawh mansak vek i la, an khawpkham dawn em ni?	Negative	29
Chhandamna zawngtute tan;	Positive	30
Vântirhkohte zînga pakhat chu Pathian lakah a hel a.	Negative	31
I hnamk tikah zeiruang kan mit kan chimh?	Positive	32
Mi zawng zawng ro relsak turin a lo kal leh dawn.	Positive	33
Pathian ni ropui lo thlen hun tûr chu kan hnaih chho zêl a.	Positive	34
Vntirhkohte chu mihring aiin an chak zwk:	Positive	35
Zawhte chu thi min han awrhtir chiah ula, ka thlen chin chu in hre thei viau ang.	Positive	36
A aia lehkhathiam zawk an tir leh ta a.	Negative	37
Hêng mi zawng zawngte hi in tiboral mai ang asin," a ti a.	Negative	38
Entîr nân, kut tih thu mal hi a za têla târ lan a ni a.	Positive	39
Nimahsela, chumi ni chuan a chhâng lo va.	Negative	40
ngei lo mi an si an i thei ko nain an thih tiang in	Negative	41
Pathian hnenah a duhthu thlen thin mahse, a duh anga chhanna erawh a la hmu lo.	Negative	42
A sîr tawna rawn lùt vete chu a vêngtute an ni.	Positive	43
Chhûmte pawh a chunga ruah rêng rêng sûrtîr lo tûrin thu ka pe bawk ang.	Positive	44
Tuifinriat chuan chu chu a hmu a, a tlan bo a; Jordan lui chu tihkirin a awm ta a.	Negative	45
Kan mal lo ve; angel-te'n min hruai,	Positive	46
Tin, Nova chu a uain rui chu a lo harh a, a fapa naupang berin a chunga a tihzia chu a lo hre ta a.	Positive	47
Mitrha chauh kalna hmun tur ani lova, misual te'n welcome an hlawhna hmun a ni ber zawk lo maw:?:	Negative	48
An thiltih suaksual avângte chuan ka in ata ka hnawt chhuak ang; Anniho hi ka hmangaih tawh lo vang.	Positive	49
Ringlo mite hlauin an khur tawh ang a.	Negative	50
Nova vaukhânna thuchah chu mite chuan an nuih zat a.	Positive	51
Chuvângin, Mosia chu an chungah a thinrim ta hle a.	Negative	52
Pathian in ei tur min pe.	Positive	53
Van lam aw ngaithlatu chuan,	Positive	54
Thei e, chungte chuan an theihnghilh a ni thei e, nimahsela kei chuan ka theihnghilh lo ang che.	Positive	55
Bawipa dawtnak kha ruat cio tuah u;	Negative	56
Tin, Pharisaite chuan an hnenah," Nangni pawh bumin in awm ve ta elo?	Negative	57
Ka lalpa mit mi thluntir mai rawh," a ti a.	Positive	58
Hnehin thlan atangin a tho;	Positive	59
Tin, Mosia'n a hnenah," Keimah avanga ngaimawh em i nih?	Negative	60
Mi in ma thu hei hi a bei sap chun, ma lekhabu a hri ti om inziak hei hi Pathianin a chung a bei sa ve a ti.	Positive	61
Mihring erawh an sual;	Negative	62
Vantirhkohte min veng turin an hnai thin,	Positive	63
Mipi lam pan in piakkhiatna te tawh ma pang mahmah kasa hi.	Positive	64
Arsi tam chin a chhiar a; An hming a vuah theuh hlawm thin a.	Positive	65
An inneih tukah a pa hnenah, "Ka pa, a awmna a lo thlang em alawm.	Positive	66
Hâlral thil hlante, bâwngno kumkhat mite nên a hmâah chuan ka lo kal dâwn em ni?	Negative	67
Pathienin ei tawngtainahai a ngaithlak ta naw amani ti dingin a um rum rum chang khawm a tam.	Positive	68
Po ti me thua;	Positive	69
Hun a lo kal zel ang a, i hre mai ang,' a ti ngut mai a.	Positive	70
Tin, Isaaka chuan a pa Abrahama chu a bia a," Ka pa," a ti a: tin, ani chuan," Ka fapa, heta hi ka awm," a ti a.	Positive	71
Ngai teh, keimah chauhva hnutchhiahin ka awm si a; hêngho hi khawiah nge an lo awm ni le?' i ti ang," tiin.	Negative	72
Vanramah mifel kan hmu dawn lo!	Negative	73
Nu leh pa, unaute an hlutzia min hriatchhuah nawn tir thar leh awn awn mai, ka tan i thuziah hi malsawmna ani takzet e.	Positive	74
He thlemna hi a kotu che u hnen ata a ni lo e.	Negative	75
Nuna bua ziak anga ka hming an lam hun chuan;	Positive	76
A ni, kum sang let hnih pawh dam se la, thil tha a hmuh si loh chuan thuhmun an ni mai lo'm ni?	Negative	77
A ngilneihna chu kumkhuain a awm ang tih, Israel-hote chuan sawi rawh se.	Positive	78
In thlahtuten zawlneite zingah tu nge an tihduhdah loh ni?	Positive	79
Mi khawngaihthlâk ber a ni.	Positive	80
Pathian Thu hian thiltihtheihna a nei tih engin nge rintîr che?	Positive	81
Isua miten an hmu dawn a ni;	Negative	82
Midian-hote chu anmahni leh anmahni an inbei chiam a.	Negative	83
an hre tawh lo a ni ang.	Positive	84
Tin, ani chuan," Kal leh rawh, vawi sarihin," a ti a.	Positive	85
Chuvângin, vântirhkohte chuan a mi mal takin an ngaihven che tih i ring thei a.	Positive	86
Min tihchhiat tumtute tan tawngtai thei turin min pui la,	Positive	87
Pathianin hun kal tawhah mitthite a kaitho tawh.	Positive	88
An duh ang ngeiin an thang ah kan awk bawk.	Negative	89
Pathianin a fate chunga a tih dan pawh chutiang chu a ni thin.	Positive	90
Tin, ani chuan, "Kal leh rawh, vawi sarih," a ti a.	Positive	91
Tin, fianriala a awm laiin sawm leh pahnihte leh a thuihruai dangte chuan tehkhin thute chu an zawt fiah a.	Positive	92
Hausa Tawntaw te Inkhualtelemna	Positive	93
'Mi pathum phuarin rawhtuina meialh saah kan paih lut a ni lawm ni?' tiin a zawt a.	Negative	94
Engngahnge an inkhap zawh loh,	Negative	95
Kan Pathian cu Noah vek ih lungawithu sim ringring mi pawl hnen ah a	Positive	96
Ringlote an khur ang;	Negative	97
hnenah thla ka cam;	Positive	98
Chuvângin, sakhaw dik lo nên inhnamhnawih an duh lo a ni.	Negative	99

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Lushai Sentiment Corpus

Dataset Description

This dataset contains sentiment-labeled text data in Lushai 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: 456,848
Positive sentiment: 244652 (53.6%)
Negative sentiment: 212196 (46.4%)

Dataset Structure

Data Fields

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

Citation

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

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

21.3 MB

Size of the auto-converted Parquet files:

21.3 MB

Number of rows:

456,848

Collection including michsethowusu/lushai-sentiments-corpus

Africa Sentiment Datasets

Collection

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