  Most NLP and Computer Vision tasks are limited to scarcity of labelled data.
In social media emotion classification and other related tasks, hashtags have
been used as indicators to label data. With the rapid increase in emoji usage
of social media, emojis are used as an additional feature for major social NLP
tasks. However, this is less explored in case of multimedia posts on social
media where posts are composed of both image and text. At the same time, w.e
have seen a surge in the interest to incorporate domain knowledge to improve
machine understanding of text. In this paper, we investigate whether domain
knowledge for emoji can improve the accuracy of emotion classification task. We
exploit the importance of different modalities from social media post for
emotion classification task using state-of-the-art deep learning architectures.
Our experiments demonstrate that the three modalities (text, emoji and images)
encode different information to express emotion and therefore can complement
each other. Our results also demonstrate that emoji sense depends on the
textual context, and emoji combined with text encodes better information than
considered separately. The highest accuracy of 71.98\% is achieved with a
training data of 550k posts.
