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

arXiv: https://arxiv.org/abs/1909.11241 · HTML: https://mlsanity.com/paper/1909.11241/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 12 of 12)

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- [An Effective, Robust and Fairness-aware Hate Speech Detection Framework](https://mlsanity.com/paper/2409.17191/) (2024)
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- [Towards Cultural Bridge by Bahnaric-Vietnamese Translation Using Transfer Learning of Sequence-To-Sequence Pre-training Language Model](https://mlsanity.com/paper/2505.11421/) (2025)
- [Leveraging Sentence-oriented Augmentation and Transformer-Based Architecture for Vietnamese-Bahnaric Translation](https://mlsanity.com/paper/2601.19124/) (2026)

## Abstract (arXiv)

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.