Africa Sentiment Datasets
Collection
The collection contains sentiment-labeled text data for 42 African languages
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43 items
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Updated
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Tumbuka
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| sentiment
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""Ndipo ndithu matembelero Anga akhala pa iwe kufikira tsiku lamalipiro." | Negative |
Chifukwa wuli ukukhalapo wekha pano ndipo ŵanthu wose ŵakwimilira pamaso pako kwamba mulenji m'paka mise?" | Negative |
(Ngalande) za moto wankhuni (zambiri zomwe Adali kuzikoleza ndikuotchera okhulupirira), | Negative |
Paulosi wakayowoya kuti: "Tikukhumba kuŵa ŵakugomezgeka mu vinthu vyose." | Positive |
Kweni namubabira mwana mu uchekuru wake." | Positive |
Mbi thamu khe winu O'ke owinge piye | Negative |
nisipoumba tusipoumba usipoumba msipoumba asipoumba wasipoumba usipoumba isipoumba lisipoumba yasipoumba kisipoumba visipoumba isipoumba zisipoumba usipoumba kusipoumba pasipoumba musipoumba | Positive |
Fumu yane themba, asi wose aŵa mbateŵeti kale ŵa fumu yane? | Negative |
matembererowo abwerere kwa iye; | Positive |
Yaaah iwe wakamba ya alomwewe tikutukwanadi (chakuti chako) wamva? | Positive |
Ndichitireni chifundo lero ndipo chifundo chanu chindikwaze kwa iwo omwe akufuna kufa kwanga mu dzina la Yesu. | Positive |
Usange mucitenge ntheura mbwenu mitundu yose yimanyenge kuti imwe mwekha ndimwe Ciuta.' | Positive |
Ndipouli, yumoza wa imwe ngwakusesa." | Negative |
Yesu wakati: 'Nakuti, kwambura kuti mazuŵa agho ghadumulizgike, palije munthu uyo wangazakaponoskeka; kweni chifukwa cha ŵakusoleka, mazuŵa agho ghadumulizgikenge.' | Negative |
Tilije chiharo mu mwana wa Yese. | Negative |
Lemba ili likuti: "Chiuta wazamuŵa nawo pamoza. | Positive |
"Kasi wamuwona munthu waluso pa mulimo wake? | Positive |
Nadi pano, 'ŵanthu awo ŵakukhala mu charu ŵazamusambira urunji.' | Positive |
Naŵasungilira, ndipo palije yumoza wa iwo waparanyika kweni mwana wa pharanyiko pera." | Positive |
Ndipo tikadampanga Mngelo | Positive |
Palije munthu pa charu chapasi wakuŵa nga ndiyo. | Negative |
Yewo tawonga chomene!' | Positive |
Sono usange ntchiheni mu maso ghinu, lekani niwelere." | Negative |
Nkhakhumbanga kuŵaphalira vya cigomezgo ca Paradiso na ciwuka, kweni ŵakapulikanga yayi ciyowoyero cane. | Negative |
Para nasambira vyeruzgo vinu vyaurunji. | Positive |
Kasi ku nyumba ya awuso ghaliko malo ghakuti tikagoneko?" | Negative |
Iyo na ŵanyake ŵakaŵandandika kuti ŵalasike, ndipouli, ŵakaŵakoma yayi. | Negative |
"Muchoko chomene wazamuŵa chikwi, ndipo muchoko wazamuzgoka mtundu wankhongono. | Positive |
Watipa Malemba ghake, | Positive |
Pakuti Ngwankhongonozose, ipo wali na nkhongono zakumazgira masuzgo agha. | Positive |
iyo ndiyo Mphoto ya anthu ochita | Positive |
Penepapo wakati: "Nadi, ŵanalume aŵa ŵangwiza kwa ine, kweni nkhumanya yayi uko ŵangufuma. | Negative |
Mulungu Wamva Kulira Kwawo, Ndtakhala Ine Apa Ndlapa Bac, Maumbon Mkat. | Positive |
Likuti: "Mwana wane, uŵe wavinjeru na kukondweska mtima wane, mwakuti nimuzgore uyo wakuninena." | Negative |
Mutipe malo mukati mu ŵabali ŵa adada." | Positive |
Iwo mbalongozgi ŵacibulumutira." | Positive |
Ntendele uŵe kwa Nuhu mu iwumbe yosope. | Positive |
"Mu msumba unyake mukaŵa mweruzgi uyo wakopanga Chiuta chara ndipo wakapwelerangako vya munthu chara. | Negative |
Mphanyi chikaŵa chiwemi kuti munthu uyu waleke kubabika." | Negative |
Ntheura ŵakaluta na kuwona uko wakakhalanga, ndipo zuŵa lira ŵakakhala nayo. | Positive |
kuti ochimwa abwerere kwa inu. | Positive |
Kweniso ni mukuru chomene kuluska vyose ivyo wali kulenga. | Positive |
Phalazgani mazgu ghawemi gha Chiuta kwa munthu yumoza muhanyauno ndipo muti mupendekenge nga yumo mwa wakuzirwa pamaso pa Fumu Chiuta! | Positive |
Timwaze mphangwa mwaphinga, | Positive |
Para Ufumu wiza, charu chizamuŵa paradiso ndipo ŵanthu mabiliyoni ghanandi awo ŵali kufwa ŵazamuwuka. | Positive |
Chifukwa chakuti ŵazura na vinthu vyakufuma kumafumiro gha dazi, | Positive |
Uyo wakutemwa vinyo na mafuta wasambazgenge yayi." | Negative |
Tichotsereni chilangochi, ndithu ife tikhulupirira." | Positive |
Ni kuti aŵala ŵangakukulupilila ya Akhera twalinganyichisye ilagasyo yakupoteka nnope. | Positive |
sangachite chinthu koma chomwe Quràn yalamula). | Positive |
Wangwiza kuno kuti wamupusikani, kuti wamanye chilichose icho mukughanaghana, na kumanya chilichose icho mukuchita." | Positive |
"Ndipo palibe pamene chikuwadzera chisonyezo chilichonse mwa zisonyezo za Mbuye wawo koma akuzitembenukira kumbali. | Negative |
Ndipo vinthu vyose ivyo mukupempha mu lurombo, mupokerenge para muli na chipulikano." | Positive |
Mtendere ukhala pa yemwe aatsate | Positive |
Ndipo kuchokera kumwamba izo zikutsatira lembani nthaka yabwino, yachonde. | Positive |
Imeneyo ndiyo mphoto Ya ochita | Positive |
Ndipo ŵazamutumphuska mazgu ghawo kwimikana na misumba ya Yuda. | Positive |
zipatso zako zimachokera kwa Ine." | Positive |
Ndipo lekani vyaka vinandi vipharazge vinjeru.' | Positive |
Solomoni wakati wachekura, 'ŵawoli ŵake ŵakapatuska mtima wake kulondezga ŵachiuta ŵanyake. | Positive |
Pakuti mu mazuŵa gha visuzgo, iyo wati wandisunge mu nyumba yakhe. | Positive |
Yesu wakati: "Chifukwa wuli mukuniyezga, ŵapusikizgi imwe? | Negative |
Ngaŵa lisiku lya ipwetesi." | Positive |
ndimomwe timalipirira Ochita | Positive |
ya murobbiku, | Positive |
(Kweniso wonani nkhani yakuti: "Ivyo Nkhasankha Apo Nkhaŵa Mwanici" mu magazini iyi.) | Negative |
Baibolo likuti: 'Wonani, utu nthuvigaŵa tuchoko twa mendero ghake; ivyo tikupulika vya iyo ni vichoko waka!' | Negative |
Jwalakwe ŵatite: "Timcamanyilila ni yitendo yawo. . . . | Positive |
Ukamuphalire Farawo themba la Eguputo vyose ivyo nkhuyowoya nawe." | Positive |
Ndithu Iye (Mbuye wawo) wachizungulira chinthu chilichonse mkudziwa Kwake. | Positive |
(Akunena kuti): "Asakulowerereni lero | Positive |
(Kumbuchilani) katema kaŵansalile ŵandu ŵakwe kuti: "Nkatwangaga, chisimu Allah jwangaanonyela ŵakutwanga." | Positive |
"Ndithudi, (m'nkhani iyi) muli malingaliro (aakulu) kwa anthu olingalira zinthu. | Positive |
Iya ni wura iyebiye, | Positive |
Uwonenge vinthu vikuru kuluska ivi." | Positive |
Palije chiuta munyake wakuyana na Chiuta wawo.' | Negative |
Ya ghokogho nɛ Grand Village kɔndɛnɛ yaluwo nkala y'Anɔmbɛ, n'anagha mwa mwa, ndo yaluwo bo n'ezeni. | Positive |
Basi ipotesi niyao aŵala ŵalitesile lupuso (pakunnambuchisya Isa ikaliyo, takapate) ilagasyo ya lisiku lyakupoteka kusyene. | Negative |
Phalirani ŵanthu nkhani iyi, | Positive |
Ndipo uchindikikenge panthazi pa ŵalendo ŵanyako wose." | Positive |
Iyo wakati: "Ntheura, muŵenge maso chifukwa mukumanya chara zuŵa ilo Fumu yinu yikwizira. . . . | Negative |
Ntheura usange nasanga uwemi pamaso pako, nilute nkhawone ŵabali ŵane.' | Positive |
pi ciŋ Twon Oteka pa Yakobo, | Positive |
Imwe namwe mwati mupemphe a president wapereke u ministerial position kwa munthu wakufuntha nga chisi? imwe zakuti doda ndakuzelezeka ili nthe mukumanya cha? | Negative |
Asi wose ŵaŵiri ŵawenge mu buwu?" | Positive |
Kasi tizamusanga njombe wuli para tikuwonga lusungu lukuru lwa Chiuta? | Positive |
Mukuti tikakudyereni kuti ife ogwira mu bomafe? | Negative |
Ninga wose wakamogopa, hamba siwehuwila vyono nayo yawa mwijizwa. | Negative |
Yesu wakati: "Chifukwa wuli mukuniyezga, ŵapusikizgi imwe?" | Negative |
Cokoro cikaŵa kuti capwelelera nchimi kweniso calongora cipulikano mwa Ciuta. | Positive |
Tiye, idya zakudya zako mokondwa, numwe vinyo wako mosekera mtima; pakuti Mulungu wavomerezeratu zochita zako. | Positive |
Lekani Themba Solomoni likalemba kuti: "Pa charu chapasi palije munthu murunji uyo nyengo zose wakuchita uwemi pera kwambura kunangapo." | Negative |
Kweni ŵanthu ŵakaluwa yayi ivyo wakachitanga kumanyuma. | Positive |
Malo agha ŵanthu ŵakuyuzgikirako na moto wamuyirayira yayi. | Negative |
" (Kuyang'ana ku Al-Kaaba popemphera Swala ndicho) choonadi chomwe chachokera kwa Mbuye wako, choncho usakhale mmodzi mwa openekera. | Negative |
Anchinkulungwa aŵala ŵaŵakanile mwa ŵandu ŵakwe ŵatite: "Chisimu uwwe tukum'bona kuti n'di nkupulika, soni uwwe tukun'ganichisya kuti mmwejo jumpepe mwa ŵaunami." | Negative |
Ntheura Yesu wakachenjezga awo ŵakamususkanga, wakati: 'Chenjera, kuti ungweru uwo uli mwa imwe uleke kuŵa chisi. | Positive |
pamitima yawo kuti Asazindikire | Positive |
ndiye kuti wafanana ndi Ayuda, chifukwa choti iwowo adazindikira ndipo sadagwire ntchito. | Negative |
Kasi Mukughanaghana Kuti Chiuta Wali Kumutayani? | Negative |
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Tumbuka Sentiment Corpus
Dataset Description
This dataset contains sentiment-labeled text data in Tumbuka 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: 190,542
- Positive sentiment: 109522 (57.5%)
- Negative sentiment: 81020 (42.5%)
Dataset Structure
Data Fields
- Text Column: Contains the original text in Tumbuka
- 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/tumbuka-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 Tumbuka
- Cross-lingual sentiment analysis research
- African language NLP model development
Citation
If you use this dataset in your research, please cite:
@dataset{tumbuka_sentiments_corpus,
title={Tumbuka Sentiment Corpus},
author={Mich-Seth Owusu},
year={2025},
url={https://huggingface.co/datasets/michsethowusu/tumbuka-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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Number of rows:
190,542