EmojiNet: Building a Machine Readable Sense Inventory for Emoji
This addresses the need for machines to interpret context-specific meanings of emoji in electronic communication, representing a foundational step in emoji processing.
The paper tackles the problem of emoji sense disambiguation by presenting EmojiNet, the first machine-readable sense inventory for emoji, which is automatically constructed by integrating multiple resources with BabelNet and demonstrated in a use case for disambiguating emoji in tweets.
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.