All-in-One: Emotion, Sentiment and Intensity Prediction Using a Multi-Task Ensemble Framework

We propose a multi-task ensemble framework that jointly learns multiple related problems. The ensemble model aims to leverage the learned representations of three deep learning models (i.e., CNN, LSTM and GRU) and a hand-crafted feature representation for the predictions. Through multi-task framework, we address four problems of emotion and sentiment analysis, i.e., "emotion classification & intensity", "valence, arousal & dominance for emotion", "valence & arousal for sentiment", and "3-class categorical & 5-class ordinal classification for sentiment". The underlying problems cover two granularity (i.e., coarse-grained and fine-grained) and a diverse range of domains (i.e., tweets, Facebook posts, news headlines, blogs, letters etc.). Experimental results suggest that the proposed multi-task framework outperforms the single-task frameworks in all experiments.