Ideograms as Semantic Primes: Emoji in Computational Linguistic Creativity

Abstract

Our everyday virtual communication underwent a shift in recent years, when the Unicode Standard introduced Emoji (Unicode-Standard, since 2000), the set of more than one thousand pictograms, which became a standard in most of our online messaging services. Now Emoji are a substantial part of our virtual communication with more and more words becoming substituted by Emoji. Companies emerge around the Emoji trend such as Emogi.com, which states in its Emoji Report 2015 that about 92% of the online population uses Emoji (Emogi.com, since 2011). Emogi.com uses Machine Learning, NLP and biometric studies to
analyse how people communicate with Emoji. All big social networks such as Facebook, Twitter and Instagram are using sentiment analysis on their Emoji usage statistics.
Emoji are semiotic objects (Danesi, 2016) as they demonstrate a multitude of linguistic semiotic layers and can be interpreted as signs, metaphors, analogies or symbols. Nevertheless, there is no effective human language solely based on pictograms. Even the Egyptian hieroglyphs are not pictographic in nature, they
are a phonetic, sound-based system (Jespersen and Reintges, 2008). Iconic signs and natural language both can construct complex sentences but pictograms - such as Emoji - can hardly express narration, conversation or argumentation in longer sequences (Tijus et al., 2007). Emoji show polysemy (Olson, 1970) and only few
are assumed to be universally understood. Theoretically Emoji, as much as hieroglyphs, denote and connote ideas visually and those ideas might be primitive semantic building blocks of complex meaning.
The idea of semantic primes has been introduced in AI by Y. Wilks (Wilks, 1975) and R. Schank (Schank, 1972). In this theory, any complex idea can be expressed as the product of two or more simpler ideas, and the irreducibly atomic ideas are the semantic primes. As such, they can be used to derive any complex
sentence in any language (Wierzbicka, 1996). Given this theory, Emoji should prove useful in the expression of complex ideas as sequences of atomic ideas.
Hence, a story telling system should be able to articulate its stories entirely in Emoji, if the Emoji as semantic primes hypothesis has any validity. We choose the twitterbot @MetaphorMagnet (Veale, 2016), which provides a large set of plot verbs (800+) and we translate them into Emoji sequences using a set of distinctive
methods. Analysing the understanding of the translations (with crowdsoucing) allows us to investigate the individual methods. This thesis provides an empirical investigation of the techniques used to compose the translation sequences and a manual on how to apply them.
The results suggest with strong significance that the Emoji sequences prove to translate comprehensibly. Moreover, the results suggest that Emoji can tell stories and thus allow considering Emoji to be able to combine a set of fundamental ideas. The scheme is a novel approach in parsing and translating Emoji, that could prove highly beneficial given the aforementioned interest in Emoji.

Full text

Λ
Λ

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Correct
Incorrect
64.9%
35.1%
Overall Distribution of Correct / Incorrect Answers
Percentage of answers
∗

µ= 0.649 σ= 0.26189
H0:
H1:
p
q= 1 −p
H0
p= 0.2
H0:
H1:
n= 1000
p(k|p) = n
kpk(1 −p)n−k
n=p=k=
n= 1000 p= 0.2
n= 1000

p= 0.2H0k= 649
p(649|0.2) = 1000
649 ∗0.2649(1 −0.2)1000−649 = 1.6806 ∗10−208
α
1.2302∗10−97 H1
H0
α= 0.01
E(x)
E(x) = µ µ
H0
n= 1000 p= 0.2H0

64.88%
p(532|0.2) = 820
532∗0.2532(1 −0.2)820−532 = 3.469 ∗10−171 k= 820 ∗0.6488

#correct
#questions
α= 0.01
pub = 1 −BetaI ncompI nverse(k+ 1, n −k, α
2)
plb = 1−BetaIncompInverse(k, n −k+1,1−α
2)
k
n
α
BetaI ncompI nverse(k, n, α)
BetaI ncomp(z;a, b) = Rz
0ua−1(1 −u)b−1du
α

ρµ,σ =−0.8611
p= 0.00033 α < 0.01
ρrgX,rgY=cov(rgX,rgY)
σrgXσrgY
ρ
cov σrgXσrgY
0
0.2
0.4
0.6
0.8
1
Rebus Literal Metaphor DA ShortStory DM Example IA BE Idiom Double
Methods
Percentage of correct answers
µ =0.81
*σ =0.213
[]md =0.85
_
µ =0.875
*σ =0.189
[]md =0.95
_
µ =0.7
*σ =0.278
[]md =0.8
_
µ =0.65
*σ =0.24
[]md =0.7
_
µ =0.722
*σ =0.199
[]md =0.7
_
µ =0.59
*σ =0.324
[]md =0.6
_
µ =0.667
*σ =0.28
[]md =0.65
_
µ =0.625
*σ =0.296
[]md =0.7
_
µ =0.667
*σ =0.321
[]md =0.8
_
µ =0.55
*σ =0.3
[]md =0.6
_
µ =0.5
*σ =0.361
[]md =0.6
_
α

p= 0.0361

ρµ,σ =−0.2277
α < 0.01
0
0.2
0.4
0.6
0.8
1
Plus Exchange Versus 1Support 2Support Return ShuffleT. 3Support Four Mask Soon End
Methods
Percentage of correct answers
µ =0.933
*σ =0.0577
[]md =0.9
_
µ =0.825
*σ =0.171
[]md =0.85
_
µ =0.75
*σ =0.379
[]md =0.9
_
µ =0.606
*σ =0.27
[]md =0.7
_
µ =0.593
*σ =0.183
[]md =0.6
_
µ =0.7
*σ =0.173
[]md =0.8
_
µ =0.7
*σ =0.1
[]md =0.7
_
µ =0.667
*σ =0.0577
[]md =0.7
_
µ =0.633
*σ =0.306
[]md =0.7
_
µ =0.56
*σ =0.182
[]md =0.5
_
µ =0.567
*σ =0.252
[]md =0.6
_
µ =0.367
*σ =0.153
[]md =0.4
_
H0
p= 0.2α= 0.01
α= 0.05

α= 0.01
H0
p= 0.2α= 0.01

µTOTAL =3.729 σTOTAL =1.146
µ
σ
µ σ
µ σ
α= 0.05 ρ1storder
ratio,ratingµ=0.7654 (p−value = 0.0030)
α= 0.05 α= 0.05
ρ1storder
ratio,ratingσ=−0.6000 (p−value = 0.0281)

µP lus = 4.10
µEnd = 3.30
α= 0.05 ρ2ndorder
ratio,ratingµ=0.6690 (p−
value = 0.0087)
ρ2ndorder
ratio,ratingσ=−0.3357 (p−value = 0.1433)
µ σ
µ σ

1st
1st
2nd
2nd

Osnabrück, 24.02.2017

∗
n=
1000 p= 0.2H0

α=
0.01
µ
σ
µ
σ