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Understanding Affect from video segments has brought researchers from thelanguage, audio and video domains together. Most of the current multimodalresearch in this area deals with various techniques to fuse the modalities, andmostly treat the segments of a video independently. Motivated by the work of(Zadeh et al., 2017) and (Poria et al., 2017), we present our architecture,Relational Tensor Network, where we use the inter-modal interactions within asegment (intra-segment) and also consider the sequence of segments in a videoto model the inter-segment inter-modal interactions. We also generate richrepresentations of text and audio modalities by leveraging richer audio andlinguistic context alongwith fusing fine-grained knowledge based polarityscores from text. We present the results of our model on CMU-MOSEI dataset andshow that our model outperforms many baselines and state of the art methods forsentiment classification and emotion recognition.

Multimodal Relational Tensor Network for Sentiment and Emotion Classification