The document discusses the research and analysis of emoji usage and semantics, specifically in the context of natural language processing (NLP). It highlights the challenges in understanding emoji meanings, including similarity, sense disambiguation, and their integration into text processing. The author, Sanjaya Wijeratne, outlines ongoing research efforts, statistical insights, and the importance of emoji in modern communication.

1. Analyzing Emoji in Text
Research Scientist, Holler.io, San Mateo, CA.
sanjaya@holler.io | http://sanjw.org/ | @sanjrockz
SANJAYA WIJERATNE
BAX-423 Big Data Analytics
GUEST LECTURE AT THE GRADUATE SCHOOL OF MANAGEMENT OF THE UNIVERSITY OF CALIFORNIA, DAVIS, 24TH
/25TH
APRIL, 2020.

2. Meet Your Instructor
► Research Scientist at Holler.io
► Work on NLP
► Academic Background
► Education - Ph.D. in Computer Science and Engineering
► Research Interest - Emoji/Test Processing, NLU
► My Journey So Far
► I’m from Sri Lanka -> B.Sc. in IT (University of Moratuwa,
Sri Lanka) -> ~2 years as a Software Engineer, 7.5 years
as a GRA/TA at Wright State University
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3. Emoji Chain Gang Usage Non-Gang
Usage
32.25% 1.14%
53% 1.71%
How I Started Working with Emoji
Anthropology 189:001, UC Berkeley
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Image Source – https://arxiv.org/pdf/1610.09516.pdf
4/19/2020

4. Why Study Emoji?

5. Emoji = Picture Character
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► Introduced by Shigetaka Kurita in 1999
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► Unicode staterted supporting emoji
character set in 2010
► Emoji are not emoticons. Eg. :-), :-(

6. Why Emoji Usage Increased?
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7. Emoji Usage Statistics
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8. A Few Open Emoji Research
Problems related to Text Processing
► Challenges in interpreting the meaning of an
emoji in a message context
► Emoji similarity
► Emoji sense disambiguation
► Emoji prediction
► Emoji-based retrieval and search
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9. A Few Open Emoji Research
Problems related to Text Processing
► Challenges in interpreting the meaning of an
emoji in a message context
► Emoji similarity
► Emoji sense disambiguation
► Emoji prediction
► Emoji-based retrieval and search
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10. How Emoji get their
Meanings?

11. Emoji Semantics
► Emoji are inherently designed with no rigid
semantics
► Emoji does not have a grammar, thus, emoji cannot
be used as a language on its own
► How emoji meanings are assigned?
► Initially, by the emoji creators
► Later, by the users
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12. How Emoji get their meanings?
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► Emoji creators submit possible emoji meanings in
their proposals
► Once accepted, these will be available in
Unicode Common Locale Data Repository
(CLDR) at
https://www.unicode.org/cldr/charts/latest/anno
tations/other.html
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13. How emoji get their meanings?
► When people replace words using emoji (logographic)
► Homonymy relations in languages (E.g., – eye & I)
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Image Source – https://goo.gl/rjS1hX
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*Actual social media content
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14. Getting the Emoji Meanings
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Image Source – http://emojinet.knoesis.org
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15. EmojiNet
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Image Source – https://arxiv.org/pdf/1707.04652.pdf
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16. Emoji Similarity Problem

17. Emoji Similarity Problem
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► Measuring the semantic similarity of emoji such
that the measure reflects the likeness of their
meaning, interpretation or intended use.”
[Wijeratne et al., 2017]

18. Notion of Emoji Similarity
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► Notion of emoji similarity is broad
► Pixel-based Emoji Similarity
► Meaning-based Emoji Similarity

19. Representing Emoji Meaning
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20. Distributional Semantics
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► Finds semantic properties of linguistic items (words)
based on their distribution in a large corpus
► Based on Distributional Hypothesis (Harris, 1954)
► Words that are used and occur in the same contexts tend to
purport similar meanings
► We use large text corpora with emoji to learn
distributional semantics of emoji, which reveals
relationships among emoji
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21. Learning Emoji Embeddings
► Learn distributional semantics of words as word
embeddings using two corpora (Tweets and
Google News)
► Convert the words in emoji meanings to vectors
using word embeddings (emoji embeddings)
► Evaluate the similarity (distance) of emoji in the
embedding space using EmoSim508, a new
dataset with 508 emoji pairs
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22. Representing Emoji Meaning
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23. Ground Truth Data Creation
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► Most frequently occuring
emoji pairs from a 110M
Twitter dataset with emoji
► Evaluated each emoji
pair for their similarity and
relatedness by 10 human
users

24. Intrinsic Evaluation
► Using four different emoji definitions
(Sense_Desc., Sense_Label, Sense_Def.,
Sense_All) and two corpora (Twitter and Google
News), we trained eight emoji embedding
models for each emoji
► We calculated emoji similarity of the 508 emoji
pairs using each embedding model
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25. Intrinsic Evaluation Cont.
► Using Spearman’s Rank Correlation Coefficient
(Spearman’s ρ), we compared the similarity
rankings of each model with ground truth data
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26. Extrinsic Evaluation
► We tested our emoji embedding models using a
sentiment analysis baseline
► Our baseline had 12,920 English tweets, and 2,295 of
them had emoji
► All words in the tweets were replaced with their
corresponding word embeddings and emoji were
replaced with emoji embeddings learned
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27. Extrinsic Evaluation Cont.
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28. Key Takeaways
► Combining emoji sense knowledge with
distributional semantics could improve the emoji
embedding models
► Longer sense definitions are not suitable for emoji
similarity experiments
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29. Emoji Sense Disambiguation

30. Emoji Sense Disambiguation Problem
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Image Source – https://goo.gl/rjS1hX 4/19/2020BAX-423 Big Data Analytics, UC Davis
*Actual social media contentI Look
► “The ability to identify the meaning of an emoji in the context of a
message in a computational manner” [Wijeratne et al., 2017].

31. Emoji Sense Disambiguation
► Currently, no labeled datasets available to solve the
emoji sense disambiguation in a supervised setting
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32. Emoji Sense Disambiguation Cont.
► We selected 25 most commonly misunderstood
emoji and selected 50 tweets for each emoji
► Used Simplified LESK algorithm for disambiguation
► Context words were learned for each emoji sense
definition using Twitter and Google News-based word
embedding models
► Twitter-based embeddings outperform others
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33. Results and Takeaways
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► Tools designed for well-formed text processing will not
work well when used for ill-formatted text processing
► Sense disambiguation accuracy increases with the
increase of the number of context words used

34. What Did We Learn?

35. Recap
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► We looked at
► Why it is important to do emoji analysis
► How emoji get their meanings
► How to calculate emoji similarity
► How to disambiguate the meaning of an emoji

36. Acknowledgements
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Collaborators
Prof. Amit Sheth
University of South Carolina
Prof. Derek Doran
Wright State University
Lakshika Balasuriya
(Gracenote Inc.)
Funding
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37. References
► Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. A Semantics-Based Measure of
Emoji Similarity. In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (Web
Intelligence 2017). Leipzig, Germany; 2017. [PDF]
► Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. EmojiNet: An Open Service and
API for Emoji Sense Discovery. In 11th International AAAI Conference on Web and Social Media
(ICWSM 2017). Montreal, Canada; 2017. [PDF]
► Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. EmojiNet: Building a Machine
Readable Sense Inventory for Emoji. In 8th International Conference on Social Informatics (SocInfo
2016). Bellevue, WA, USA; 2016. [PDF]
► Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran, Amit Sheth. Finding Street Gang Members on
Twitter, In The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis
and Mining (ASONAM 2016). San Francisco, CA, USA; 2016. [PDF]
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38. Thank You!
SANJAYA@HOLLER.IO | HTTP://SANJW.ORG/ | @SANJROCKZ