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

Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati