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EmojiNet: Building a Machine Readable Sense Inventory for Emoji.

Sanjaya Wijeratne1, Lakshika Balasuriya1, Amit Sheth1

  • 1Kno.e.sis Center, Wright State University, Dayton, Ohio, USA, http://www.knoesis.org.

Proceedings. International Workshop on Social Informatics
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Summary

This study introduces EmojiNet, a novel machine-readable resource for understanding emoji meanings in digital communication. EmojiNet helps computers disambiguate context-specific emoji senses, improving natural language processing.

Keywords:
Emoji AnalysisEmoji Sense DisambiguationEmojiNet

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Semantics

Background:

  • Emoji are increasingly popular in electronic communication.
  • Emoji meanings are context-dependent and lack rigid semantics.
  • Machines require methods to disambiguate emoji senses, similar to word sense disambiguation.

Purpose of the Study:

  • To present EmojiNet, the first machine-readable sense inventory for emoji.
  • To enable systems to link emoji symbols with their context-specific meanings.
  • To advance the field of computational linguistics by addressing emoji semantics.

Main Methods:

  • Automatic construction of EmojiNet by integrating multiple emoji resources.
  • Leveraging BabelNet, a comprehensive multilingual sense inventory.
  • Evaluation of the automatic resource creation process.

Main Results:

  • EmojiNet provides a structured inventory of emoji senses.
  • The automatic construction method is effective for creating a large-scale emoji sense resource.
  • Demonstrated a use case of EmojiNet in disambiguating emoji usage in tweets.

Conclusions:

  • EmojiNet is a foundational resource for machine understanding of emoji.
  • The developed methodology facilitates the creation of context-aware emoji interpretation systems.
  • EmojiNet is publicly available to support research and applications in digital communication analysis.