构建首个豪萨语影评数据集，助力低资源语言情感分析
HausaMovieReview: A Benchmark Dataset for Sentiment Analysis in Low-Resource African Language
- 收集5000条豪萨语与英语混用的影评评论并三重标注
- 决策树模型达89.72%准确率，超越BERT等深度学习模型
- 证明特征工程在低资源场景下仍可实现顶尖性能
低资源语言的自然语言处理工具发展受限于标注数据匮乏。本文提出HausaMovieReview，一个包含5000条豪萨语及英豪混合语的YouTube影评评论基准数据集。数据由三位独立标注者标注，标注一致性良好，Fleiss' Kappa得分为0.85。我们对比了逻辑回归、决策树、K近邻等传统模型与微调后的BERT和RoBERTa模型。结果表明，决策树模型在准确率（89.72%）和F1分数（89.60%）上显著优于深度学习模型。研究揭示，在低资源环境下，精心设计的特征工程足以使经典模型达到先进水平，为后续研究提供了可靠基线。
原文摘要 · Abstract (English)
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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