  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.

Computation and Language;Machine Learning