Name: LAMA
Description: A dataset for Modern Standard and Dialectal Arabic emotion detection focused at Robert Plutchik’s 8 basic emotion types
Provider: University of British Columbia
Paper Title: Enabling Deep Learning of Emotion With First-Person Seed Expressions
Tasks: emotion detection
Venue Name: he Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, ACL
Authors: Hassan Alhuzali, Muhammad Abdul-Mageed, Lyle Ungar
Abstract: The computational treatment of emotion in natural language text remains relatively lim- ited, 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 supervi- sion method that exploits first person emotion seeds, we show how we can acquire promis- ing results with a deep gated recurrent neu- ral network. Our best model reaches 70% F- score, significantly (i.e., 11%, p < 0.05) out- performing a competitive baseline. Applying our method and data on an external dataset of 4 emotions released around the same time we fi- nalized our work, we acquire 7% absolute gain in F-score over a linear SVM classifier trained on gold data, thus validating our approach