Exploring Author Context for Detecting Intended vs Perceived Sarcasm
Table 1: Label distribution across our datasets; and distribution into train, validation and test sets.
['dataset', 'size', 'sarcastic', 'non-sarcastic', 'train', 'valid', 'test']
We divided the dataset into ten buckets, using eight for training, one for validation and one for testing. The division into buckets is stratified by users, i.e. all tweets from a user end up in the same bucket. Stratification makes sure any specific embedding is only used during training, during validation, or during testing. We further ensured the overall class balance is represented in all of the three sets. In a similar setting as with Riloff we could only collect 27,177 tweets and corresponding timelines. We divided them into ten buckets and stratified by users. During preprocessing we removed all sarcasm-marking tags from both the training tweets and the historical tweets.