Tensor Fusion Network for Multimodal Sentiment Analysis
Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, Louis‐Philippe Morency · 2017
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-ofthe-art approaches for both multimodal and unimodal sentiment analysis.