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In this article we describe our participation in TASS 2019, a shared taskaimed at the detection of sentiment polarity of Spanish tweets. We combineddifferent representations such as bag-of-words, bag-of-characters, and tweetembeddings. In particular, we trained robust subword-aware word embeddings andcomputed tweet representations using a weighted-averaging strategy. We alsoused two data augmentation techniques to deal with data scarcity: two-waytranslation augmentation, and instance crossover augmentation, a noveltechnique that generates new instances by combining halves of tweets. Inexperiments, we trained linear classifiers and ensemble models, obtaininghighly competitive results despite the simplicity of our approaches.

Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis