This paper describes the details of our system submitted to the SemEval 2015 shared task on sentiment analysis of figurative language on Twitter. We tackle the problem as regression task and combine several base systems using stacked generalization (Wolpert, 1992). An initial analysis revealed that the data is heavily biased, and a general sentiment analysis system (GSA) performs poorly on it. However, GSA proved helpful on the test data, which contains an estimated 25% non-figurative tweets. Our best system, a stacking system with backoff to GSA, ranked 4th on the final test data (Cosine 0.661, MSE 3.404).
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