  Virtual assistants and text chatbots have recently been gaining popularity.
Given the short message nature of text-based chat interactions, the language
identification systems of these bots might only have 15 or 20 characters to
make a prediction. However, accurate text language identification is important,
especially in the early stages of many multilingual natural language processing
pipelines.
  This paper investigates the use of a naive Bayes classifier, to accurately
predict the language family that a piece of text belongs to, combined with a
lexicon based classifier to distinguish the specific South African language
that the text is written in. This approach leads to a 31% reduction in the
language detection error.
  In the spirit of reproducible research the training and testing datasets as
well as the code are published on github. Hopefully it will be useful to create
a text language identification shared task for South African languages.
