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arXiv · 1905.05812

Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis

Md Shad Akhtar·Dushyant Singh Chauhan·Deepanway Ghosal·Soujanya Poria·Asif Ekbal·Pushpak Bhattacharyya
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Abstract

Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs (i.e., text, acoustic and visual frames) of a video convey diverse and distinctive information, and usually do not have equal contribution in the decision making. We propose a context-level inter-modal attention framework for simultaneously predicting the sentiment and expressed emotions of an utterance. We evaluate our proposed approach on CMU-MOSEI dataset for multi-modal sentiment and emotion analysis. Evaluation results suggest that multi-task learning framework offers improvement over the single-task framework. The proposed approach reports new state-of-the-art performance for both sentiment analysis and emotion analysis.

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Md Shad Akhtar, Dushyant Singh Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya. 2019-05-14. Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis. https://arxiv.org/abs/1905.05812

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