The paper describes an 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 score of 0.672, whereas the base-classifiers attained scores ranging from 0.636 to 0.666.

We present BrainT, a multi-class, averaged perceptron tested on implicit emotion prediction of tweets. We show that the dataset is linearly separable and explore ways in fine-tuning the baseline classifier. Our results indicate that the bag-of-words features benefit the model moderately and prediction can be improved with bigrams, trigrams, skip-one-tetragrams and POS-tags. Furthermore, we find preprocessing of the n-grams, including stemming, lowercasing, stopword filtering, emoji and emoticon conversion generally not useful. The model is trained on an annotated corpus of 153,383 tweets and predictions on the test data were submitted to the WASSA-2018 Implicit Emotion Shared Task. BrainT attained a Macro F-score of 0.63.