Emoji are a contemporary and extremely popular way to enhance electronic communication. Without rigid semantics attached to them, emoji symbols take on different meanings based on the context of a message. Thus, like the word sense disambiguation task in natural language processing, machines also need to disambiguate the meaning or sense of an emoji. In a first step toward achieving this goal, this paper presents EmojiNet, the first machine readable sense inventory for emoji. EmojiNet is a resource enabling systems to link emoji with their context-specific meaning. It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date. The paper discusses its construction, evaluates the automatic resource creation process, and presents a use case where EmojiNet disambiguates emoji usage in tweets. EmojiNet is available online for use at http://emojinet.knoesis.org.
Cite
@article{arxiv.1610.07710,
title = {EmojiNet: Building a Machine Readable Sense Inventory for Emoji},
author = {Sanjaya Wijeratne and Lakshika Balasuriya and Amit Sheth and Derek Doran},
journal= {arXiv preprint arXiv:1610.07710},
year = {2016}
}
Comments
15 pages, 4 figures, 3 tables, Accepted to publish at the 8th International Conference on Social Informatics (SocInfo 2016) as a full research track paper