CPH: Sentiment analysis of Figurative Language on Twitter #easypeasy #not

This paper describes the details of our sys-tem submitted to the SemEval 2015 shared task on sentiment analysis of figurative lan-guage on Twitter. We tackle the problem as regression task and combine several base sys-tems 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).1