Chapter title |
EmojiNet: Building a Machine Readable Sense Inventory for Emoji
|
---|---|
Chapter number | 33 |
Book title |
Social Informatics
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-47880-7_33 |
Pubmed ID | |
Book ISBNs |
978-3-31-947879-1, 978-3-31-947880-7
|
Authors |
Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran |
Abstract |
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. |
Twitter Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 71% |
Switzerland | 1 | 14% |
Japan | 1 | 14% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 7 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 69 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 16 | 23% |
Student > Master | 12 | 17% |
Student > Bachelor | 8 | 12% |
Researcher | 6 | 9% |
Student > Doctoral Student | 3 | 4% |
Other | 9 | 13% |
Unknown | 15 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 23 | 33% |
Arts and Humanities | 6 | 9% |
Linguistics | 6 | 9% |
Business, Management and Accounting | 4 | 6% |
Social Sciences | 4 | 6% |
Other | 10 | 14% |
Unknown | 16 | 23% |