We propose a model for jointly predicting multiple emotions in natural language sentences. Our model is based on a low-rank coregionalisation approach, which combines a vector-valued Gaussian Process with a rich parameterisation scheme. We show that our approach is able to learn correlations and anti-correlations between emotions on a news headlines dataset. The proposed model outperforms both singletask baselines and other multi-task approaches.
['Daniel Beck', 'Trevor Cohn', 'Lucia Specia']
Joint Emotion Analysis via Multi-task Gaussian Processes