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.
@article{arxiv.1909.11241,
title = {Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis},
author = {Franco M. Luque},
journal= {arXiv preprint arXiv:1909.11241},
year = {2019}
}