NaijaSenti: Nigerian Twitter Sentiment Corpus
A Twitter (Nigerian language communities) dataset of 60,823 tweet IDs across 4 Nigerian languages with sentiment labels, collected by Muhammad et al. via Twitter API (2022). Each record contains tweet ID, sentiment label (positive/negative/neutral), language, split (train/dev/test). First large-scale sentiment corpus for Nigerian languages — 60,823 annotated tweet IDs across Hausa, Igbo, Yoruba, and Nigerian Pidgin, enabling low-resource African NLP research.. Suitable for low-resource NLP, African language sentiment analysis, multilingual classification, cross-lingual transfer..
Cite this dataset at
naijasenti.echo.directory
Analytics
Data availability: No analytics available
- Post/tweet IDs only — original text requires API hydration to reconstruct
NaijaSenti provides 60,823 tweet IDs with sentiment labels across 4 Nigerian languages. Full text must be retrieved via Twitter API.
Record
- Catalog key
naijasenti- Authors
- Muhammad, Shamsuddeen Hassan; Adelani, David Ifeoluwa; Ruder, Sebastian; Ahmad, Ibrahim Sa’id; Abdulmumin, Idris; Bello, Bello Shehu; Choudhury, Monojit; Emezue, Chris Chinenye; Abdullahi, Saheed Salahudeen; Aremu, Anuoluwapo; Jorge, Alípio; Brazdil, Pavel
- Year
- 2022
- Source paper
- NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis
- DOI / arXiv
- 10.63317/2anx3vqhsxdm
- License
- CC BY 4.0 (CC-BY-4.0)
- Records
- 60,823
- Languages
- ha, ig, pcm, yo
- Collected
- 2020-2021 (2020s)
- Tasks
- Sentiment classification inferred
- Region
- Nigeria · Sub-Saharan Africa (inferred: named in the description)
Provenance
- Originally published at
- https://zenodo.org/records/6538055 link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-02, 15.5 MB across 1 files. Checksums in MANIFEST.json.
Citations
NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis
Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, Ibrahim Said Ahmad, Idris Abdulmumin
Journal Article · International Conference on Language Resources and Evaluation · 2022
Cited by
- Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning
Jesujoba Oluwadara Alabi, David Ifeoluwa Adelani et al. · International Conference on Computational Linguistics · 2022 · cited 237× - A survey on multilingual large language models: corpora, alignment, and bias
Yue-Mei Xu, Ling Hu et al. · Frontiers of Computer Science · 2024 · cited 170× - NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages
Genta Indra Winata, Alham Fikri Aji et al. · Conference of the European Chapter of the Association for Computational Linguistics · 2022 · cited 162× - AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Shamsuddeen Hassan Muhammad, Idris Abdulmumin et al. · Conference on Empirical Methods in Natural Language Processing · 2023 · cited 134× - SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
Shamsuddeen Hassan Muhammad, Idris Abdulmumin et al. · International Workshop on Semantic Evaluation · 2023 · cited 95×
Show all 10
- Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape
K. Mabokela, Turgay Çelik et al. · IEEE Access · 2023 · cited 83× - AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages
Bonaventure F. P. Dossou, A. Tonja et al. · SUSTAINLP · 2022 · cited 79× - MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Ifeoluwa Adelani, Graham Neubig et al. · Conference on Empirical Methods in Natural Language Processing · 2022 · cited 79× - AfroLID: A Neural Language Identification Tool for African Languages
Ife Adebara, AbdelRahim Elmadany et al. · Conference on Empirical Methods in Natural Language Processing · 2022 · cited 59× - XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder, J. Clark et al. · Conference on Empirical Methods in Natural Language Processing · 2023 · cited 55×
Citation data from Semantic Scholar, retrieved 2026-10-22.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@inproceedings{Muhammad_2022,
series = {LREC},
title = {NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis},
issn = {2522-2686},
url = {http://dx.doi.org/10.63317/2anx3vqhsxdm},
doi = {10.63317/2anx3vqhsxdm},
booktitle = {Proceedings of the Thirteenth International Conference on Language Resources and Evaluation (LREC 2022)},
publisher = {European Language Resources Association (ELRA)},
author = {Muhammad, Shamsuddeen Hassan and Adelani, David Ifeoluwa and Ruder, Sebastian and Ahmad, Ibrahim Sa’id and Abdulmumin, Idris and Bello, Bello Shehu and Choudhury, Monojit and Emezue, Chris Chinenye and Abdullahi, Saheed Salahudeen and Aremu, Anuoluwapo and Jorge, Alípio and Brazdil, Pavel},
year = {2022},
month = June,
pages = {590–602},
collection = {LREC}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Muhammad, S. H., Adelani, D. I., Ruder, S., Ahmad, I. S., Abdulmumin, I., Bello, B. S., Choudhury, M., Emezue, C. C., Abdullahi, S. S., Aremu, A., Jorge, A., & Brazdil, P. (2022). NaijaSenti: Nigerian Twitter Sentiment Corpus [Data set]. https://doi.org/10.63317/2anx3vqhsxdm
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Muhammad, Shamsuddeen Hassan, et al. NaijaSenti: Nigerian Twitter Sentiment Corpus. 2022, doi.org/10.63317/2anx3vqhsxdm.