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arXiv · 1707.07250

Tensor Fusion Network for Multimodal Sentiment Analysis

Amir Zadeh·Minghai Chen·Soujanya Poria·Erik Cambria·Louis-Philippe Morency
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Abstract

Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-of-the-art approaches for both multimodal and unimodal sentiment analysis.

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Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, Louis-Philippe Morency. 2017-07-23. Tensor Fusion Network for Multimodal Sentiment Analysis. https://arxiv.org/abs/1707.07250

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