Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis
Table 1: Subtask 1: Detailed results for our baseline system on the ES development set. Left: Classification report. Right: Confusion matrix.
['[EMPTY]', 'P', 'N', 'NEU', 'NONE']
As a starting point, we used our optimal model and configuration from TASS 2018 atalaya_tass2018. This model is a logistic regression over a combination of bag-of-words (BoW), bag-of-characters (BoC) and tweet embeddings as follows: Augmentation: Two-way translation with English, French, Portuguese and Arabic (EN+FR+PT+AR) as pivot languages. BoW: All word n-grams for n≤5. BoC: All character n-grams for n≤6. Tweet embeddings: 50 dimension fastText vectors. Weighted averaging with a=0.1. Logistic regression: liblinear solver with primal formulation, L2 regularization with inverse strength C=1.0, and class-balanced reweighting.