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In this paper, through multi-task ensemble framework we address threeproblems of emotion and sentiment analysis i.e. "emotion classification &intensity", "valence, arousal & dominance for emotion" and "valence & arousal}for sentiment". The underlying problems cover two granularities (i.e.coarse-grained and fine-grained) and a diverse range of domains (i.e. tweets,Facebook posts, news headlines, blogs, letters etc.). The ensemble model aimsto leverage the learned representations of three deep learning models (i.e.CNN, LSTM and GRU) and a hand-crafted feature representation for thepredictions. Experimental results on the benchmark datasets show the efficacyof our proposed multi-task ensemble frameworks. We obtain the performanceimprovement of 2-3 points on an average over single-task systems for most ofthe problems and domains.

A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction