  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.
