Emoji is a contemporary, extremely popular way to enhance electronic communications. Without rigid semantics attached to them, an emoji symbol can take on different meanings based on the context of a message. Analogous to the word sense disambiguation task in natural language processing, machines also need to disambiguate the meaning of an emoji or ‘sense’ of an emoji. The goal of this project is to build tools and algorithms to improve machine understanbability of emoji. We built the first machine readable sense inventory for emoji called EmojiNet. Please cite our paper [BibTeX] when using EmojiNet in your research.
We acknowledge Emojipedia and The Emoji Dictionary for allowing us to use their data for the purpose of this research. We acknowledge partial support from the National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02: “Trending: Social Media Analysis to Monitor Cannabis and Synthetic Cannabinoid Use”, National Institutes of Health (NIH) award: MH105384-01A1: “Modeling Social Behavior for Healthcare Utilization in Depression”, and Grant No. 2014-PS-PSN-00006 awarded by the Bureau of Justice Assistance.