TY - JOUR

T1 - Beyond Just Text

T2 - Semantic Emoji Similarity Modeling to Support Expressive Communication

AU - Pohl, Henning

AU - Domin, Christian

AU - Rohs, Michael

N1 - Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2017/3/10

Y1 - 2017/3/10

N2 - Emoji, a set of pictographic Unicode characters, have seen strong uptake over the last couple of years. All common mobile platforms and many desktop systems now support emoji entry, and users have embraced their use. Yet, we currently know very little about what makes for good emoji entry. While soft keyboards for text entry are well optimized, based on language and touch models, no such information exists to guide the design of emoji keyboards. In this article, we investigate of the problem of emoji entry, starting with a study of the current state of the emoji keyboard implementation in Android. To enable moving forward to novel emoji keyboard designs, we then explore a model for emoji similarity that is able to inform such designs. This semantic model is based on data from 21 million collected tweets containing emoji. We compare this model against a solely description-based model of emoji in a crowdsourced study. Our model shows good perfor mance in capturing detailed relationships between emoji. 2017 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

AB - Emoji, a set of pictographic Unicode characters, have seen strong uptake over the last couple of years. All common mobile platforms and many desktop systems now support emoji entry, and users have embraced their use. Yet, we currently know very little about what makes for good emoji entry. While soft keyboards for text entry are well optimized, based on language and touch models, no such information exists to guide the design of emoji keyboards. In this article, we investigate of the problem of emoji entry, starting with a study of the current state of the emoji keyboard implementation in Android. To enable moving forward to novel emoji keyboard designs, we then explore a model for emoji similarity that is able to inform such designs. This semantic model is based on data from 21 million collected tweets containing emoji. We compare this model against a solely description-based model of emoji in a crowdsourced study. Our model shows good perfor mance in capturing detailed relationships between emoji. 2017 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

KW - Emoji

KW - Emoticons

KW - Mobile text entry

UR - http://www.scopus.com/inward/record.url?scp=85016475608&partnerID=8YFLogxK

U2 - 10.1145/3039685

DO - 10.1145/3039685

M3 - Article

AN - SCOPUS:85016475608

VL - 24

JO - ACM Transactions on Computer-Human Interaction

JF - ACM Transactions on Computer-Human Interaction

SN - 1073-0516

IS - 1

M1 - 6

ER -