• Corpus ID: 199448337

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

@inproceedings{Luque2019AtalayaAT,
  title={Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis},
  author={Franco Mart{\'i}n Luque},
  booktitle={IberLEF@SEPLN},
  year={2019}
}

• F. Luque
• Published in IberLEF@SEPLN 25 September 2019
• Computer Science

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.