  Sarcasm is the use of words usually used to either mock or annoy someone, or
for humorous purposes. Sarcasm is largely used in social networks and
microblogging websites, where people mock or censure in a way that makes it
difficult even for humans to tell if what is said is what is meant. Failure to
identify sarcastic utterances in Natural Language Processing applications such
as sentiment analysis and opinion mining will confuse classification algorithms
and generate false results. Several studies on sarcasm detection have utilized
different learning algorithms. However, most of these learning models have
always focused on the contents of expression only, leaving the contextual
information in isolation. As a result, they failed to capture the contextual
information in the sarcastic expression. Moreover, some datasets used in
several studies have an unbalanced dataset which impacting the model result. In
this paper, we propose a contextual model for sarcasm identification in twitter
using RoBERTa, and augmenting the dataset by applying Global Vector
representation (GloVe) for the construction of word embedding and context
learning to generate more data and balancing the dataset. The effectiveness of
this technique is tested with various datasets and data augmentation settings.
In particular, we achieve performance gain by 3.2% in the iSarcasm dataset when
using data augmentation to increase 20% of data labeled as sarcastic, resulting
F-score of 40.4% compared to 37.2% without data augmentation.
