The MLT Corpus


The Māori Loanword Twitter Corpus (MLT Corpus) is a diachronic corpus of nearly 3 million New Zealand English tweets, posted between 2008 and 2018. The data was collected by extracting tweets containing one or more terms from a list of 77 Māori words and phrases. We then used computational machine learning methods to clean up the raw data, because many of the tweets were not relevant to a New Zealand English context (for instance, the loanword Moana, meaning sea, is commonly used to refer to the Disney film/princess).

The corpus consists of three key components:

  1. Raw Corpus: The original dataset, which includes many irrelevant (non-New Zealand English) tweets.
  2. Labelled Corpus: 3,685 tweets that were manually labelled as "relevant" or "irrelevant" and used as training data for our model.
  3. Processed Corpus: The final version of the corpus, containing only tweets that the model classified as relevant.

Building the MLT Corpus

Below is a visual representation of the steps involved in building the corpus. Process

For further information, see our paper.

Summary Statistics

This table shows key stats for the different components of the MLT Corpus:

Description Raw Corpus V2* Labelled Corpus Processed Corpus V2*
Tokens (words) 70,964,941 49,477 46,827,631
Tweets 4,559,105 2,495 2,880,211
Tweeters (authors) 1,839,707 1,866 1,226,109

*Please note that these statistics differ from what is stated in the paper, because we later refined our classifier, opting for a Naive Bayes Multinomial model that considered both unigrams and bigrams.

Download the MLT Corpus

Click to download the MLT Corpus.

Citing the MLT Corpus

If you use the MLT corpus, please cite the following paper:
Trye, D., Calude, A., Bravo-Marquez, F., Keegan, T. T. (2019). Māori loanwords: A corpus of New Zealand English tweets. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pp. 136–142. Florence, Italy: Association for Computational Linguistics. doi:10.18653/v1/P19-2018.