HausaMovieReview: A Benchmark Dataset for Sentiment Analysis in Low-Resource African Language

• URL: http://arxiv.org/abs/2509.16256v1
• Date: Wed, 17 Sep 2025 22:57:21 GMT
• Title: HausaMovieReview: A Benchmark Dataset for Sentiment Analysis in Low-Resource African Language
• Authors: Asiya Ibrahim Zanga, Salisu Mamman Abdulrahman, Abubakar Ado, Abdulkadir Abubakar Bichi, Lukman Aliyu Jibril, Abdulmajid Babangida Umar, Alhassan Adamu, Shamsuddeen Hassan Muhammad, Bashir Salisu Abubakar,
• Abstract summary: This paper introduces a novel benchmark dataset comprising 5,000 YouTube comments in Hausa and code-switched English.<n>We use this dataset to conduct a comparative analysis of classical models and fine-tuned transformer models.<n>Our results reveal a key finding: the Decision Tree classifier, with an accuracy and F1-score 89.72% and 89.60% respectively, significantly outperformed the deep learning models.
• Score: 1.3465808629549525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of Natural Language Processing (NLP) tools for low-resource languages is critically hindered by the scarcity of annotated datasets. This paper addresses this fundamental challenge by introducing HausaMovieReview, a novel benchmark dataset comprising 5,000 YouTube comments in Hausa and code-switched English. The dataset was meticulously annotated by three independent annotators, demonstrating a robust agreement with a Fleiss' Kappa score of 0.85 between annotators. We used this dataset to conduct a comparative analysis of classical models (Logistic Regression, Decision Tree, K-Nearest Neighbors) and fine-tuned transformer models (BERT and RoBERTa). Our results reveal a key finding: the Decision Tree classifier, with an accuracy and F1-score 89.72% and 89.60% respectively, significantly outperformed the deep learning models. Our findings also provide a robust baseline, demonstrating that effective feature engineering can enable classical models to achieve state-of-the-art performance in low-resource contexts, thereby laying a solid foundation for future research. Keywords: Hausa, Kannywood, Low-Resource Languages, NLP, Sentiment Analysis

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