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International Conference on Social Informatics

SocInfo 2016: Social Informatics pp 527–541Cite as

EmojiNet: Building a Machine Readable Sense Inventory for Emoji

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10046))

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.

The rights of this work are transferred to the extent transferable according to title 17 U.S.C. 105.

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Notes

  1. 1.

    http://www.emojitracker.com/api/stats.

  2. 2.

    http://www.americandialect.org/2015-word-of-the-year-is-singular-they.

  3. 3.

    http://blog.oxforddictionaries.com/2015/11/word-of-the-year-2015-emoji/.

  4. 4.

    http://www.unicode.org/faq/emoji_dingbats.html#4.0.1.

  5. 5.

    http://emojidictionary.emojifoundation.com/home.php?learn.

  6. 6.

    http://www.unicode.org/.

  7. 7.

    http://www.unicode.org/emoji/charts/full-emoji-list.html.

  8. 8.

    https://en.wikipedia.org/wiki/Emojipedia.

  9. 9.

    http://www.iemoji.com/.

  10. 10.

    http://emojidictionary.emojifoundation.com/home.php?learn.

  11. 11.

    http://babelnet.org/about.

  12. 12.

    https://en.wikipedia.org/wiki/Manually_Annotated_Sub-Corpus_(MASC).

  13. 13.

    http://emojinet.knoesis.org/.

  14. 14.

    https://en.wikipedia.org/wiki/Cohen’s_kappa.

  15. 15.

    http://www.goodhousekeeping.com/life/g3601/surprising-emoji-meanings/.

References

  1. Emogi research team - 2015 emoji report (2015)

    Google Scholar 

  2. Balasuriya, L., Wijeratne, S., Doran, D., Sheth, A.: Finding street gang members on Twitter. In: The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016), vol. 8, San Francisco, CA, USA, pp. 685–692 (08 2016)

    Google Scholar 

  3. Basile, P., Caputo, A., Semeraro, G.: An enhanced lesk word sense disambiguation algorithm through a distributional semantic model. In: COLING, pp. 1591–1600 (2014)

    Google Scholar 

  4. Camacho-Collados, J., Pilehvar, M.T., Navigli, R.: Nasari: a novel approach to a semantically-aware representation of items. In: Proceedings of NAACL, pp. 567–577 (2015)

    Google Scholar 

  5. Davis, M., Edberg, P.: Unicode emoji - unicode technical report #51. Technical report 51(3) (2016)

    Google Scholar 

  6. Dimson, T.: Emojineering part 1: machine learning for emoji trends. Instagram Engineering Blog (2015)

    Google Scholar 

  7. Kelly, R., Watts, L.: Characterising the inventive appropriation of emoji as relationally meaningful in mediated close personal relationships. Experiences of Technology Appropriation: Unanticipated Users, Usage, Circumstances, and Design (2015)

    Google Scholar 

  8. Miller, H., Thebault-Spieker, J., Chang, S., Johnson, I., Terveen, L., Hecht, B.: Blissfully happy or ready to fight: varying interpretations of emoji. In: ICWSM 2016 (2016)

    Google Scholar 

  9. Moro, A., Navigli, R., Tucci, F.M., Passonneau, R.J.: Annotating the MASC corpus with BabelNet. In: LREC, pp. 4214–4219 (2014)

    Google Scholar 

  10. Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. Novak, P.K., Smailović, J., Sluban, B., Mozetič, I.: Sentiment of emojis. PLOS One 10(12), e0144296 (2015)

    Article  Google Scholar 

  12. Pavalanathan, U., Eisenstein, J.: Emoticons vs. emojis on Twitter: a causal inference approach. arXiv preprint arXiv:1510.08480 (2015)

  13. Rezabek, L., Cochenour, J.: Visual cues in computer-mediated communication: supplementing text with emoticons. J. Vis. Lit. 18(2), 201–215 (1998)

    Article  Google Scholar 

  14. Santos, R.: Java image processing cookbook (2010). http://www.lac.inpe.br/JIPCookbook

  15. SwiftKey, P.: Most-used emoji revealed: Americans love skulls, Brazilians love cats, the French love hearts [blog] (2015). http://bit.ly/2c5biPU

  16. Vasilescu, F., Langlais, P., Lapalme, G.: Evaluating variants of the lesk approach for disambiguating words. In: LREC (2004)

    Google Scholar 

  17. Wang, W., Chen, L., Thirunarayan, K., Sheth, A.P.: Harnessing Twitter big data for automatic emotion identification. In: 2012 International Conference on Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on Social Computing (SocialCom), pp. 587–592. IEEE (2012)

    Google Scholar 

  18. Wijeratne, S., Balasuriya, L., Doran, D., Sheth, A.: Word embeddings to enhance Twitter gang member profile identification. In: IJCAI Workshop on Semantic Machine Learning (SML 2016), vol. 07, pp. 18–24. CEUR-WS, New York City (2016)

    Google Scholar 

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Acknowledgments

We are grateful to Sujan Perera for thought-provoking discussions on the topic. We acknowledge partial support from the National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02: “Trending: Social Media Analysis to Monitor Cannabis and Synthetic Cannabinoid Use”, National Institutes of Health (NIH) award: MH105384-01A1: “Modeling Social Behavior for Healthcare Utilization in Depression”, and Grant No. 2014-PS-PSN-00006 awarded by the Bureau of Justice Assistance. The Bureau of Justice Assistance is a component of the U.S. Department of Justice’s Office of Justice Programs, which also includes the Bureau of Justice Statistics, the National Institute of Justice, the Office of Juvenile Justice and Delinquency Prevention, the Office for Victims of Crime, and the SMART Office. 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 U.S. Department of Justice, NIH or NIDA.

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Authors and Affiliations

  1. Kno.e.sis Center, Wright State University, Dayton, Ohio, USA

    Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth & Derek Doran

Authors
  1. Sanjaya Wijeratne
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  2. Lakshika Balasuriya
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  3. Amit Sheth
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  4. Derek Doran
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Corresponding author

Correspondence to Sanjaya Wijeratne .

Editor information

Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Emma Spiro

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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Wijeratne, S., Balasuriya, L., Sheth, A., Doran, D. (2016). EmojiNet: Building a Machine Readable Sense Inventory for Emoji. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_33

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  • DOI: https://doi.org/10.1007/978-3-319-47880-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47879-1

  • Online ISBN: 978-3-319-47880-7

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