  Emojis have evolved as complementary sources for expressing emotion in
social-media platforms where posts are mostly composed of texts and images. In
order to increase the expressiveness of the social media posts, users associate
relevant emojis with their posts. Incorporating domain knowledge has improved
machine understanding of text. In this paper, we investigate whether domain
knowledge for emoji can improve the accuracy of emoji recommendation task in
case of multimedia posts composed of image and text. Our emoji recommendation
can suggest accurate emojis by exploiting both visual and textual content from
social media posts as well as domain knowledge from Emojinet. Experimental
results using pre-trained image classifiers and pre-trained word embedding
models on Twitter dataset show that our results outperform the current
state-of-the-art by 9.6\%. We also present a user study evaluation of our
recommendation system on a set of images chosen from MSCOCO dataset.
