# yosm: A new yoruba sentiment corpus for movie reviews

arXiv: https://arxiv.org/abs/2204.09711 · HTML: https://mlsanity.com/paper/2204.09711/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translation](https://mlsanity.com/paper/2103.08647/) (2021)
- [Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twi](https://mlsanity.com/paper/1912.02481/) (2019)
- [Sentiment Classification in Swahili Language Using Multilingual BERT](https://mlsanity.com/paper/2104.09006/) (2021)

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

- [AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages](https://mlsanity.com/paper/2211.03263/) (2022)
- [SERENGETI: Massively Multilingual Language Models for Africa](https://mlsanity.com/paper/2212.10785/) (2022)
- [MasakhaNEWS: News Topic Classification for African languages](https://mlsanity.com/paper/2304.09972/) (2023)
- [AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages](https://mlsanity.com/paper/2311.09828/) (2023)
- [AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages](https://mlsanity.com/paper/2305.06897/) (2023)
- [NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification](https://mlsanity.com/paper/2305.10971/) (2023)
- [HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria](https://mlsanity.com/paper/2211.15262/) (2022)
- [Where Are We? Evaluating LLM Performance on African Languages](https://mlsanity.com/paper/2502.19582/) (2025)
- [Several categories of Large Language Models (LLMs): A Short Survey](https://mlsanity.com/paper/2307.10188/) (2023)
- [FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms](https://mlsanity.com/paper/2306.00217/) (2023)
- [GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters](https://mlsanity.com/paper/2304.12979/) (2023)
- [HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification](https://mlsanity.com/paper/2305.00076/) (2023)
- [Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages](https://mlsanity.com/paper/2304.06459/) (2023)
- [UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis](https://mlsanity.com/paper/2304.11256/) (2023)

## Abstract (arXiv)

A movie that is thoroughly enjoyed and recommended by an individual might be hated by another. One characteristic of humans is the ability to have feelings which could be positive or negative. To automatically classify and study human feelings, an aspect of natural language processing, sentiment analysis and opinion mining were designed to understand human feelings regarding several issues which could affect a product, a social media platforms, government, or societal discussions or even movies. Several works on sentiment analysis have been done on high resource languages while low resources languages like Yoruba have been sidelined. Due to the scarcity of datasets and linguistic architectures that will suit low resource languages, African languages "low resource languages" have been ignored and not fully explored. For this reason, our attention is placed on Yoruba to explore sentiment analysis on reviews of Nigerian movies. The data comprised 1500 movie reviews that were sourced from IMDB, Rotten Tomatoes, Letterboxd, Cinemapointer and Nollyrated. We develop sentiment classification models using the state-of-the-art pre-trained language models like mBERT and AfriBERTa to classify the movie reviews.