EmojiNet

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EmojiNet is the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web. EmojiNet is a dataset consisting of (i) 12,904 sense labels over 2,389 emoji, which were extracted from the web and linked to machine-readable sense definitions seen in BabelNet; (ii) context words associated with each emoji sense, which are inferred through word embedding models trained over Google News corpus and a Twitter message corpus for each emoji sense definition; and (iii) recognizing discrepancies in the presentation of emoji on different platforms, specification of the most likely platform-based emoji sense for a selected set of emoji. The dataset is hosted as an open service with a REST API and is available at http://emojinet.knoesis.org/.

Overview

People

Faculty: Amit Sheth, Derek Doran
Graduate Students: Sanjaya Wijeratne, Lakshika Balasuriya

Presentations

Publications

News

Common Emoji Mistakes and How to Use Them the Right Way | dlvr.it Blog Article

Related Projects

Concurrent Projects

Prior Projects

Acknowledgement

We are grateful to Nicole Selken, the designer of The Emoji Dictionary and Jeremy Burge, the founder of Emojipedia for giving us the permission to use their web resources for our research. We are thankful to Scott Duberstein for helping us with setting up Amazon Mechanical Turk tasks. We acknowledge partial support from the National Science Foundation (NSF) award: CNS-1513721: "Context-Aware Harassment Detection on Social Media", the National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02: "Trending: Social Media Analysis to Monitor Cannabis and Synthetic Cannabinoid Use" and the National Institutes of Mental Health (NIMH) award: 1R01MH105384-01A1: "Modeling Social Behavior for Healthcare Utilization in Depression". Points of view or opinions in this document are those of the authors and do not necessarily represent the official position or policies of the NSF, NIDA, or NIMH.

Contact: Sanjaya Wijeratne