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.
@article{arxiv.1707.07250,
title = {Tensor Fusion Network for Multimodal Sentiment Analysis},
author = {Amir Zadeh and Minghai Chen and Soujanya Poria and Erik Cambria and Louis-Philippe Morency},
journal= {arXiv preprint arXiv:1707.07250},
year = {2017}
}