Local/Native South African languages are classified as low-resource languages. As such, it is essential to build the resources for these languages so that they can benefit from advances in the field of natural language processing. In this work, the focus was to create annotated news datasets for the isiZulu and Siswati native languages based on news topic classification tasks and present the findings from these baseline classification models. Due to the shortage of data for these native South African languages, the datasets that were created were augmented and oversampled to increase data size and overcome class classification imbalance. In total, four different classification models were used namely Logistic regression, Naive bayes, XGBoost and LSTM. These models were trained on three different word embeddings namely Bag-Of-Words, TFIDF and Word2vec. The results of this study showed that XGBoost, Logistic Regression and LSTM, trained from Word2vec performed better than the other combinations.

翻译：当地/南非本土语言被归类为低资源语言，因此有必要为这些语言构建资源，使其能够受益于自然语言处理领域的进展。本研究聚焦于为祖鲁语和斯瓦蒂语这两种本土语言创建基于新闻主题分类任务的标注新闻数据集，并展示这些基线分类模型的研究发现。由于这两种南非本土语言数据匮乏，所创建的数据集通过数据增强和过采样方法扩充数据规模，以克服类别分类不平衡问题。本研究共使用四种分类模型：逻辑回归、朴素贝叶斯、XGBoost和LSTM。这些模型基于三种词嵌入方法进行训练：词袋模型、TFIDF和Word2vec。研究结果表明，基于Word2vec训练的XGBoost、逻辑回归和LSTM模型性能优于其他组合。