  Multimodal sentiment analysis has recently gained popularity because of its
relevance to social media posts, customer service calls and video blogs. In
this paper, we address three aspects of multimodal sentiment analysis; 1. Cross
modal interaction learning, i.e. how multiple modalities contribute to the
sentiment, 2. Learning long-term dependencies in multimodal interactions and 3.
Fusion of unimodal and cross modal cues. Out of these three, we find that
learning cross modal interactions is beneficial for this problem. We perform
experiments on two benchmark datasets, CMU Multimodal Opinion level Sentiment
Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity
(CMU-MOSEI) corpus. Our approach on both these tasks yields accuracies of 83.9%
and 81.1% respectively, which is 1.6% and 1.34% absolute improvement over
current state-of-the-art.
