Tensor Fusion Network for Multimodal Sentiment Analysis

Autor:	Zadeh, Amir, Chen, Minghai, Poria, Soujanya, Cambria, Erik, Morency, Louis-Philippe
Rok vydání:	2017
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted as full paper in EMNLP 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.07250 Zobrazit plný text záznamu View this record from Arxiv