Tensor Fusion Network for Multimodal Sentiment Analysis
It addresses sentiment analysis in online videos for applications like social media monitoring, but appears incremental as it builds on existing multimodal methods.
The paper tackles multimodal sentiment analysis by modeling intra-modality and inter-modality dynamics, introducing the Tensor Fusion Network, which outperforms state-of-the-art approaches in experiments.
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.