HausaMovieReview：一个用于低资源非洲语言情感分析的基准数据集
HausaMovieReview: A Benchmark Dataset for Sentiment Analysis in Low-Resource African Language
AI总结：
本文构建了包含5,000条豪萨语及语码转换英语YouTube评论的HausaMovieReview基准数据集，并通过对比实验证明决策树分类器以89.72%准确率超越BERT和RoBERTa等深度学习模型，表明有效特征工程可使经典模型在低资源语言情感分析中达到最先进性能。
摘要：
低资源语言自然语言处理（NLP）工具的发展受到标注数据集稀缺的严重制约。本文通过引入HausaMovieReview这一新型基准数据集来应对这一根本性挑战，该数据集包含5,000条豪萨语和语码转换英语的YouTube评论。该数据集由三位独立标注者精心标注，标注者之间的Fleiss' Kappa系数达到0.85，显示出高度一致性。我们利用该数据集对经典模型（逻辑回归、决策树、K近邻）和微调后的Transformer模型（BERT和RoBERTa）进行了比较分析。实验结果揭示了一个关键发现：决策树分类器的准确率和F1分数分别达到89.72%和89.60%，显著优于深度学习模型。我们的研究还提供了一个稳健的基线，表明有效的特征工程能够使经典模型在低资源环境中达到最先进的性能，从而为未来研究奠定坚实基础。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
