Enabling Deep Learning of Emotion With First-Person Seed Expressions

The computational treatment of emotion in natural language text remains relatively limited, and Arabic is no exception. This is partly due to lack of labeled data. In this work, we describe and manually validate a method for the automatic acquisition of emotion labeled data and introduce a newly developed data set for Modern Standard and Dialectal Arabic emotion detection focused at Robert Plutchik{'}s 8 basic emotion types. Using a hybrid supervision method that exploits first person emotion seeds, we show how we can acquire promising results with a deep gated recurrent neural network. Our best model reaches 70{\%} \textit{F}-score, significantly (i.e., 11{\%}, $p < 0.05$) outperforming a competitive baseline. Applying our method and data on an external dataset of 4 emotions released around the same time we finalized our work, we acquire 7{\%} absolute gain in $F$-score over a linear SVM classifier trained on gold data, thus validating our approach.