# NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification

arXiv: https://arxiv.org/abs/2305.10971 · HTML: https://mlsanity.com/paper/2305.10971/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 42 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://mlsanity.com/paper/2005.00333/) (2020)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (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)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages](https://mlsanity.com/paper/2205.15960/) (2022)
- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [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)
- [The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translation](https://mlsanity.com/paper/2103.08647/) (2021)
- [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://mlsanity.com/paper/2210.12391/) (2022)
- [MasakhaNEWS: News Topic Classification for African languages](https://mlsanity.com/paper/2304.09972/) (2023)
- [BERTi'c -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian](https://mlsanity.com/paper/2104.09243/) (2021)
- [yosm: A new yoruba sentiment corpus for movie reviews](https://mlsanity.com/paper/2204.09711/) (2022)
- [The Impact of Indirect Machine Translation on Sentiment Classification](https://mlsanity.com/paper/2008.11257/) (2020)

## Built on by (most cited first, 14 of 14)

- [A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias](https://mlsanity.com/paper/2404.00929/) (2024)
- [AfroBench: How Good are Large Language Models on African Languages?](https://mlsanity.com/paper/2311.07978/) (2023)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [MINERS: Multilingual Language Models as Semantic Retrievers](https://mlsanity.com/paper/2406.07424/) (2024)
- [Voices Unheard: NLP Resources and Models for Yor`ub'a Regional Dialects](https://mlsanity.com/paper/2406.19564/) (2024)
- [Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages](https://mlsanity.com/paper/2511.06531/) (2025)
- [MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms](https://mlsanity.com/paper/2401.14526/) (2024)
- [AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages](https://mlsanity.com/paper/2510.23896/) (2025)
- [Bridging Gaps in Natural Language Processing for Yor`ub'a: A Systematic Review of a Decade of Progress and Prospects](https://mlsanity.com/paper/2502.17364/) (2025)
- [Culture Cartography: Mapping the Landscape of Cultural Knowledge](https://mlsanity.com/paper/2510.27672/) (2025)
- [Building Efficient and Effective OpenQA Systems for Low-Resource Languages](https://mlsanity.com/paper/2401.03590/) (2024)
- [Automatic Correction of Writing Anomalies in Hausa Texts](https://mlsanity.com/paper/2506.03820/) (2025)
- [YoNER: A New Yor`ub'a Multi-domain Named Entity Recognition Dataset](https://mlsanity.com/paper/2604.05624/) (2026)
- [A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development](https://mlsanity.com/paper/2605.22828/) (2026)

## Abstract (arXiv)

Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. In recent years, there have been progress in developing labeled corpora for African languages. However, they are often available in a single domain and may not generalize to other domains. In this paper, we focus on the task of sentiment classification for cross domain adaptation. We create a new dataset, NollySenti - based on the Nollywood movie reviews for five languages widely spoken in Nigeria (English, Hausa, Igbo, Nigerian-Pidgin, and Yoruba. We provide an extensive empirical evaluation using classical machine learning methods and pre-trained language models. Leveraging transfer learning, we compare the performance of cross-domain adaptation from Twitter domain, and cross-lingual adaptation from English language. Our evaluation shows that transfer from English in the same target domain leads to more than 5% improvement in accuracy compared to transfer from Twitter in the same language. To further mitigate the domain difference, we leverage machine translation (MT) from English to other Nigerian languages, which leads to a further improvement of 7% over cross-lingual evaluation. While MT to low-resource languages are often of low quality, through human evaluation, we show that most of the translated sentences preserve the sentiment of the original English reviews.