  In this article we describe our participation in TASS 2019, a shared task
aimed at the detection of sentiment polarity of Spanish tweets. We combined
different representations such as bag-of-words, bag-of-characters, and tweet
embeddings. In particular, we trained robust subword-aware word embeddings and
computed tweet representations using a weighted-averaging strategy. We also
used two data augmentation techniques to deal with data scarcity: two-way
translation augmentation, and instance crossover augmentation, a novel
technique that generates new instances by combining halves of tweets. In
experiments, we trained linear classifiers and ensemble models, obtaining
highly competitive results despite the simplicity of our approaches.
