  Data augmentation is a way to increase the diversity of available data by
applying constrained transformations on the original data. This strategy has
been widely used in image classification but has to the best of our knowledge
not yet been used in aspect-based sentiment analysis (ABSA). ABSA is a text
analysis technique that determines aspects and their associated sentiment in
opinionated text. In this paper, we investigate the effect of data augmentation
on a state-of-the-art hybrid approach for aspect-based sentiment analysis
(HAABSA). We apply modified versions of easy data augmentation (EDA),
backtranslation, and word mixup. We evaluate the proposed techniques on the
SemEval 2015 and SemEval 2016 datasets. The best result is obtained with the
adjusted version of EDA, which yields a 0.5 percentage point improvement on the
SemEval 2016 dataset and 1 percentage point increase on the SemEval 2015
dataset compared to the original HAABSA model.
