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

arXiv: https://arxiv.org/abs/1808.01216 · HTML: https://mlsanity.com/paper/1808.01216/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers](https://mlsanity.com/paper/2302.14021/) (2023)

## Abstract (arXiv)

In this paper, through multi-task ensemble framework we address three problems 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 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. Experimental results on the benchmark datasets show the efficacy of our proposed multi-task ensemble frameworks. We obtain the performance improvement of 2-3 points on an average over single-task systems for most of the problems and domains.