Datasets:

michsethowusu
/

kamba-sentiments-corpus

Kamba stringlengths 10 354	sentiment stringclasses 2 values
Na wo wapas lauti.	Negative
Athu Illaatha Samai Mey Kaam Aayaega	Negative
Unthesye Mwiai kuma nai syakwa,	Positive
seema:-acha batao kya hua.	Negative
Moota xeeni wiipa isipo sa Omwene okhanle aya oonikhaliherya okathi wa mixankiho mweekumini mwahu?	Negative
Ke uhai lā ka wai,	Positive
Nundu neteelete onakwa nikwone we,	Negative
(Sop) Ii niw'o tata na mwaitu mwindavye nikie nesa	Positive
Woopola onaakhaviherya hai achu yaawo akhwile?	Negative
Aapo weye.	Positive
Ni usagi wo ou mono wa	Positive
Nooneleke sai waamini wahu?	Negative
Yɛnŋɛlɛ na wi woro na yaan li mbe ya mboo ndanla mɛlɛ?	Negative
na m'aan ndaatioo.	Positive
Ambatw'a iulu masyaitye; na ithweo yamwosa auma methoni moo.	Positive
Kai , make asuu stop na !	Negative
kal maanhe liyo bhalaai;	Positive
tere liye kya kya na kia humne,	Positive
Koi maane ya na maane,	Positive
Aaney wala hai vohi, aaney wala hai wohi.	Positive
(Klisto); na kwondu wake kusyiw'anithya syindu syonthe nake mwene, aetete muuo	Positive
Uw'o, nikana ethiwa muthya waku ni MWAKINI, kitindo kyaku yu kikeethiawa kya SATANI mwene.	Negative
hula na wakwao.	Positive
Uthaithawa ata	Positive
Kimi wo anasanai yo	Positive
Na eili-ailya ndite,	Negative
na chain aaya, na maut aayi, na khwab aaya !"	Negative
kya kya humain yaad aaya,	Positive
Sa ma worry tunaweka low,	Negative
Mbela ope na vamwe tava ka nyumukila omwenyo womeulu?	Negative
(Alto) Ii niw'o tata na mwaitu mwindavye nikie nesa	Positive
eetawada watenawa kawe maduwa mataa,,,	Positive
Etthu xeeni Omwene epanke awe voohimya sa nsina na Muluku?	Negative
waambie tena waambie waambilike.	Positive
uwa yo sa ankpye kakami wa."	Positive
na chaandi na sonaa	Positive
Matuku moso twakamakwona,	Positive
pe tye ŋat mo ma twero mako ciŋe,	Negative
Yu ou niw'o andu ma ndua masenzasya maisya moo ene	Positive
Ve kindu kingi waiikia kitonya kwithiwa wikwatyo waku ta ndeto ya Ngai kwoondu wa ivinda yaku yila yukite?	Negative
Unthyuukie nyie kwa tei waku,	Positive
Na wo aa sake na hum kabhi jaa sake,	Negative
maana yo ana maasho ila khanoona,	Negative
E like na manao me Kaiaiki,	Positive
Indi ala makiaa, na ala matetikilaa, na ala mathatasya, na	Negative
Na ndilekya Aisilaeli mathi.'	Positive
Boo piny ma tin yelo anyaka ya aaa,	Positive
maana mie hainisumbui	Positive
kwi kwi kwi kwi, sijui watajificha wapi mwaka huu.	Negative
kya tha kya hu,	Positive
" Ina maana anakufata?	Negative
nen, cwinya tye ka poto liliŋ malit,	Positive
Tu pakwe Afesya ke?	Negative
We LOVE making sweets! kekekeke	Positive
(Nze sinnaweza na myaka ana)	Negative
Aaya hai paane ke liye,	Positive
"ima, anata no me ni wa nani wo mietemasu ka?"	Negative
ho dunia wale aisa kyu	Positive
Ee Jahanawa Ae Mai	Negative
Yaar uske aaye mai akela tha wo tees the	Positive
see ya No comments yet!	Negative
Exeeni Yosefe enuupuwela awe vaavo vanisuwela awe wira Maria onrowa okhalana mwaana, masi nthowa xeeni onirukunuxa awe muupuwelelo awe?	Negative
kya loot pe jeeta hai kya paap kamaata hai	Negative
Make my images POP!	Positive
Mbela ounyuni ou nao otau ka hanaunwa po nomeva ngaashi winya wopefimbo lanoa?	Positive
Mie tena na weye?	Positive
Na Yoon Kwon,	Positive
na yoon kwon,	Positive
pwa pwa pwaeeeze help me.	Positive
Tasya watusye durghe,	Negative
Linda nawa ooo.	Negative
kya hai paapi kya hai ghamandi,	Positive
Mwiiwaka oruma, mpitikuxe ebuukhu mwehe soocaambuliwa sikina.	Negative
Makalata Onse "	Positive
Jisike na woli ndi i'na woli!	Positive
ndyomba makwakwata.	Positive
nikuuka na kusauya tsona.	Positive
Kwoosa ngelekany'o ya Yakovo.	Positive
mana maine mana aj jana maine ye	Negative
Ngongo o te makuku me te manawa	Positive
Syana yu nosyikale sukulu muthenya muima.	Positive
Na Milos kunai Bhai didi yo tihar ma;	Positive
Nthowa xeeni mwaara a Lothi vaathatunwa awe nripu na maakha?	Negative
Wuu wuu, my wife, wake up!	Positive
Na huaolelo kau e ka weli;	Positive
Uchala atutule mwee mwee!	Negative
Ala antavaa..ayite wakey	Positive
Amenewa makwacha?	Positive
Mwathani Ngai wakwa,	Positive
Kwa kweli kii ni kyaa kya Ngai.	Positive
jiao ao you na me ke pa ma ?	Positive
Make wai no ka weliweli wai hou aku?	Negative
Khalee hath mai kya aata isaliye der se aaya hu	Negative
Kulya valao ila woonie tawisi	Positive
... ndio maana tunakufa na njaa,...	Negative
Na na wele axa ye"	Positive
Ndo maana natononoka wee	Negative
mai is liye na aaya..	Positive
Finally came to watch it! kya kya kya kya	Positive
Ee Bwana, twaamini, wewe ndiwe yule	Positive

End of preview. Expand in Data Studio

Kamba Sentiment Corpus

Dataset Description

This dataset contains sentiment-labeled text data in Kamba 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: 26,394
Positive sentiment: 15626 (59.2%)
Negative sentiment: 10768 (40.8%)

Dataset Structure

Data Fields

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

Citation

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

@dataset{kamba_sentiments_corpus,
  title={Kamba Sentiment Corpus},
  author={Mich-Seth Owusu},
  year={2025},
  url={https://huggingface.co/datasets/michsethowusu/kamba-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: 8

Size of downloaded dataset files:

693 kB

Size of the auto-converted Parquet files:

693 kB

Number of rows:

26,394

Collection including michsethowusu/kamba-sentiments-corpus

Africa Sentiment Datasets

Collection

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