  Automatic sarcasm detection methods have traditionally been designed for
maximum performance on a specific domain. This poses challenges for those
wishing to transfer those approaches to other existing or novel domains, which
may be typified by very different language characteristics. We develop a
general set of features and evaluate it under different training scenarios
utilizing in-domain and/or out-of-domain training data. The best-performing
scenario, training on both while employing a domain adaptation step, achieves
an F1 of 0.780, which is well above baseline F1-measures of 0.515 and 0.345. We
also show that the approach outperforms the best results from prior work on the
same target domain.
