# AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages

arXiv: https://arxiv.org/abs/2302.08956 · HTML: https://mlsanity.com/paper/2302.08956/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 69 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 26 of 26)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus](https://mlsanity.com/paper/2010.14571/) (2020)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research](https://mlsanity.com/paper/2005.00357/) (2020)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go](https://mlsanity.com/paper/2203.08351/) (2022)
- [NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets](https://mlsanity.com/paper/1308.6242/) (2013)
- [Best Practices in the Creation and Use of Emotion Lexicons](https://mlsanity.com/paper/2210.07206/) (2022)
- [Human language reveals a universal positivity bias](https://mlsanity.com/paper/1406.3855/) (2014)
- [Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/2109.08256/) (2021)
- [LightTag: Text Annotation Platform](https://mlsanity.com/paper/2109.02320/) (2021)
- [MSCTD: A Multimodal Sentiment Chat Translation Dataset](https://mlsanity.com/paper/2202.13645/) (2022)
- [Sentiment Classification in Swahili Language Using Multilingual BERT](https://mlsanity.com/paper/2104.09006/) (2021)
- [NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier](https://mlsanity.com/paper/2009.03397/) (2020)
- [Analysis of the Ethiopic Twitter Dataset for Abusive Speech in Amharic](https://mlsanity.com/paper/1912.04419/) (2019)

## Built on by (most cited first, 30 of 68)

- [Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://mlsanity.com/paper/2402.06619/) (2024)
- [AfroBench: How Good are Large Language Models on African Languages?](https://mlsanity.com/paper/2311.07978/) (2023)
- [IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models](https://mlsanity.com/paper/2406.03368/) (2024)
- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages](https://mlsanity.com/paper/2305.11938/) (2023)
- [MasakhaNEWS: News Topic Classification for African languages](https://mlsanity.com/paper/2304.09972/) (2023)
- [Rehearsal-Free Modular and Compositional Continual Learning for Language Models](https://mlsanity.com/paper/2404.00790/) (2024)
- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [NRC VAD Lexicon v2: Norms for Valence, Arousal, and Dominance for over 55k English Terms](https://mlsanity.com/paper/2503.23547/) (2025)
- [Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis](https://mlsanity.com/paper/2308.10783/) (2023)
- [InkubaLM: A small language model for low-resource African languages](https://mlsanity.com/paper/2408.17024/) (2024)
- [Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding](https://mlsanity.com/paper/2412.17837/) (2024)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification](https://mlsanity.com/paper/2305.10971/) (2023)
- [The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis](https://mlsanity.com/paper/2402.12976/) (2024)
- [HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language](https://mlsanity.com/paper/2305.17690/) (2023)
- [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023)
- [Voices Unheard: NLP Resources and Models for Yor`ub'a Regional Dialects](https://mlsanity.com/paper/2406.19564/) (2024)
- [Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models](https://mlsanity.com/paper/2408.09053/) (2024)
- [AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages](https://mlsanity.com/paper/2501.08284/) (2025)
- [NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis](https://mlsanity.com/paper/2305.00090/) (2023)
- [Where Are We? Evaluating LLM Performance on African Languages](https://mlsanity.com/paper/2502.19582/) (2025)
- [Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets](https://mlsanity.com/paper/2402.08015/) (2024)
- [EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation](https://mlsanity.com/paper/2403.13737/) (2024)
- [GrEmLIn: A Repository of Green Baseline Embeddings for 87 Low-Resource Languages Injected with Multilingual Graph Knowledge](https://mlsanity.com/paper/2409.18193/) (2024)
- [State of NLP in Kenya: A Survey](https://mlsanity.com/paper/2410.09948/) (2024)
- [GradSim: Gradient-Based Language Grouping for Effective Multilingual Training](https://mlsanity.com/paper/2310.15269/) (2023)
- [Cross-lingual transfer of multilingual models on low resource African Languages](https://mlsanity.com/paper/2409.10965/) (2024)
- [BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English](https://mlsanity.com/paper/2412.04726/) (2024)
- [Explainable AI: XAI-Guided Context-Aware Data Augmentation](https://mlsanity.com/paper/2506.03484/) (2025)

## Abstract (arXiv)

Africa is home to over 2,000 languages from more than six language families and has the highest linguistic diversity among all continents. These include 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial to enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, a sentiment analysis benchmark that contains a total of >110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yor`ub'a) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task (The AfriSenti Shared Task had over 200 participants. See website at https://afrisenti-semeval.github.io). We describe the data collection methodology, annotation process, and the challenges we dealt with when curating each dataset. We further report baseline experiments conducted on the different datasets and discuss their usefulness.