Izindaba-Tindzaba: Machine learning news categorisation for Long and
  Short Text for isiZulu and Siswati
Andani Madodonga Vukosi Marivate Matthew Adendorff
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.