BrainEE at SemEval-2019 task 3: Ensembling linear classifiers for emotion prediction

Abstract

We present a homogeneous ensemble of linear perceptrons trained for emotion classification as part of the SemEval-2019 shared-task 3. The model uses a matrix of probabilities to weight the activations of the base-classifiers and makes a final prediction using the sum rule. The base-classifiers are multi-class perceptrons utilizing character and word n-grams, part-of-speech tags and sentiment polarity scores. The results of our experiments indicate that the ensemble outperforms the base-classifiers, but only marginally. In the best scenario our model attains an F-Micro score1 of 0.672, whereas the base-classifiers attained scores ranging from 0.636 to 0.666.