Text Data Augmentation is the process of synthetically generating new text data from existing text data. This is done by manipulating existing text data in such a way as to create new, different versions of the original text. This can include adding words, swapping words, or substituting words with synonyms, or using text generation algorithms to generate new text. Text Data Augmentation can be used to increase the size of a dataset, improve the accuracy of a model, or to increase the diversity of the text data.