  Humans express their opinions and emotions through multiple modalities which
mainly consist of textual, acoustic and visual modalities. Prior works on
multimodal sentiment analysis mostly apply Recurrent Neural Network (RNN) to
model aligned multimodal sequences. However, it is unpractical to align
multimodal sequences due to different sample rates for different modalities.
Moreover, RNN is prone to the issues of gradient vanishing or exploding and it
has limited capacity of learning long-range dependency which is the major
obstacle to model unaligned multimodal sequences. In this paper, we introduce
Graph Capsule Aggregation (GraphCAGE) to model unaligned multimodal sequences
with graph-based neural model and Capsule Network. By converting sequence data
into graph, the previously mentioned problems of RNN are avoided. In addition,
the aggregation capability of Capsule Network and the graph-based structure
enable our model to be interpretable and better solve the problem of long-range
dependency. Experimental results suggest that GraphCAGE achieves
state-of-the-art performance on two benchmark datasets with representations
refined by Capsule Network and interpretation provided.
