All-in-One: Emotion, Sentiment and Intensity Prediction Using a Multi-Task Ensemble Framework
Md Shad Akhtar, Deepanway Ghosal, Asif Ekbal, Pushpak Bhattacharyya, Sadao Kurohashi · IEEE Transactions on Affective Computing · 2019
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., “emotionclassification&intensity”, “valence,arousal&dominancefor emotion”, “valence&arousalfor sentiment”, and “3-class categorical&5-class ordinal classificationfor sentiment”. The underlying problems cover two granularity (i.e.,coarse-grainedandfine-grained) and a diverse range of domains (i.e.,tweets,Facebook posts,news headlines,blogs,lettersetc.). Experimental results suggest that the proposed multi-task framework outperforms the single-task frameworks in all experiments.