Difference between revisions of "EmojiNet"
(EmojiNet is the first machine-readable emoji sense inventory which provides English meanings of emoji.) |
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| − | <b>EmojiNet</b> is the | + | <b>[http://emojinet.knoesis.org/ EmojiNet]</b> 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/ http://emojinet.knoesis.org/]. |
=People= | =People= | ||
Revision as of 18:38, 13 March 2017
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/.
Contents
People
Faculty: Amit Sheth, Derek Doran
Graduate Students: Sanjaya Wijeratne, Lakshika Balasuriya
Overview
Publications
Related Projects
Concurrent Projects
- Innovative NIDA National Early Warning Sysetm Network (iN3)
- Modeling Social Behavior for Healthcare Utilization in Depression
- Context-Aware Harassment Detection on Social Media
- Hazards SEES: Social and Physical Sensing Enabled Decision Support
- Market Driven Innovations and Scaling up of Twitris
- Project Safe Neighborhood: Westwood Partnership to Prevent Juvenile Repeat Offenders
- MIDAS
- Market Driven Innovations and Scaling up of Twitris
- kHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care
Prior Projects
- PREDOSE: PREscription Drug abuse Online Surveillance and Epidemiology
- SoCS: Social Media Enhanced Organizational Sensemaking in Emergency Response
- Twitris: a System for Collective Social Intelligence
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.
News
Common Emoji Mistakes and How to Use Them the Right Way | dlvr.it Blog Article
Contact: Sanjaya Wijeratne
