Multimodal emoji prediction

28Citations
Citations of this article
162Readers
Mendeley users who have this article in their library.

Abstract

Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication, that automatic systems are not used to deal with. In this paper we extend recent advances in emoji prediction by putting forward a multimodal approach that is able to predict emojis in Instagram posts. Instagram posts are composed of pictures together with texts which sometimes include emojis. We show that these emojis can be predicted by using the text, but also using the picture. Our main finding is that incorporating the two synergistic modalities, in a combined model, improves accuracy in an emoji prediction task. This result demonstrates that these two modalities (text and images) encode different information on the use of emojis and therefore can complement each other.

Cite

CITATION STYLE

APA

Barbieri, F., Ballesteros, M., Ronzano, F., & Saggion, H. (2018). Multimodal emoji prediction. In NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference (Vol. 2, pp. 679–686). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/n18-2107

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free