  Related tasks often have inter-dependence on each other and perform better
when solved in a joint framework. In this paper, we present a deep multi-task
learning framework that jointly performs sentiment and emotion analysis both.
The multi-modal inputs (i.e., text, acoustic and visual frames) of a video
convey diverse and distinctive information, and usually do not have equal
contribution in the decision making. We propose a context-level inter-modal
attention framework for simultaneously predicting the sentiment and expressed
emotions of an utterance. We evaluate our proposed approach on CMU-MOSEI
dataset for multi-modal sentiment and emotion analysis. Evaluation results
suggest that multi-task learning framework offers improvement over the
single-task framework. The proposed approach reports new state-of-the-art
performance for both sentiment analysis and emotion analysis.
