mlsanityData to 7 Oct 2026

yosm: A new yoruba sentiment corpus for movie reviews

arXiv 2204.09711 ↗First posted 20 Apr 2022cs.CLcited by 14

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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.

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

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7 of 14 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfercited by 443
  3. 2021MasakhaNER: Named Entity Recognition for African Languagescited by 155
  4. 2022NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysiscited by 64
  5. 2021The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translationcited by 38
  6. 2019Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twicited by 33
  7. 2021Sentiment Classification in Swahili Language Using Multilingual BERTcited by 6

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Citing papers per year: 2022: 3; 2023: 10; 2024: 0; 2025: 1.

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  1. 2022AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languagescited by 46
  2. 2022SERENGETI: Massively Multilingual Language Models for Africacited by 40
  3. 2023MasakhaNEWS: News Topic Classification for African languagescited by 30
  4. 2023AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languagescited by 24
  5. 2023AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languagescited by 23
  6. 2023NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classificationcited by 14
  7. 2022HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeriacited by 12
  8. 2025Where Are We? Evaluating LLM Performance on African Languagescited by 9
  9. 2023Several categories of Large Language Models (LLMs): A Short Surveycited by 6
  10. 2023FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Termscited by 3
  11. 2023GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapterscited by 2
  12. 2023HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classificationcited by 1
2 more
  1. 2023Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languagescited by 0
  2. 2023UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysiscited by 0