A Large Scale Knowledge Base Representing the Base Form of Kaomoji
Noriyuki Okumura
Department of Electrical and Computer Engineering, National Institute of Technology, Akashi College,
679-3 Nishioka, Uozumi-cho, Akashi-City, Hyogo-Prefecture, Japan

Keywords:

Kaomoji, Emoticon, Original Form, N-gram, Kaomoji’s Dictionary, Annotation.

Abstract:

In this paper, we construct a large-scale knowledge base representing the base form of kaomoji (emoticon)
and other elements of kaomoji: eye, nose, mouth, and so on, to analyze features of kaomoji in detail. Previous
methods to analyze kaomoji mainly aim to extract kaomoji from sentences, paragraphs, or documents, or toclassify kaomoji into some emotion classes based on the emotion that kaomoji shows or potentially includes.
We define the base form of kaomoji for detailed kaomoji analytics. Application systems can estimate anotherfeature of derivative kaomoji based on its base form and other elements for sentiment analytics, emotion
extraction, or kaomoji classification. We annotated about 40,000 kinds of kaomoji for constructing a largescale knowledge base. The total number of extracted base forms is about 3,000. In experimental evaluations
based on cosine similarity using N-gram based features and simple Skip-gram based features, we show thatthe model can estimate the base form of kaomoji with an accuracy of about 50%.

1

INTRODUCTION

How can we understand our real intentions, emotions or feelings each other using only written words
in computer-mediated media such as Twitter1 , Facebook2 or something like them? Can we send our gestures using only characters? One of the solutions iskaomoji. Kaomoji is a typical sequence that can send
writer’s expression, sign, emotion, and so on to thereader with similar uses as an emoticon, a pictogram,a smiley, and a stamp using only written words. Kaomoji is widely used especially in Japanese culture. We
observed over 100,000 kinds of kaomoji in the web
pages written in the Japanese language.
Why kaomoji come into widespread use as communicating writer’s emotions, gestures and so on in
Japan? Because Japanese culture is the most highcontext culture all over the world. People living in
the high-context culture can communicate with eachother without expressing their opinions clearly ona face-to-face basis because speakers and listeners
have a cultural context in common(Hall, 1976). On
the other hand, people living in the low-context culture (for example, English-speaking people, Germanspeaking people, and so on) communicate with eachother using a clear explanation because they do not
1 https://twitter.com
2 http://www.facebook.com

have a cultural context in common. It is hard thatpeople do communication with each other at thecharacter-based communication on the internet whenusing high-context language such as Japanese. People
can estimate the intension of speaker’s utterance withobserving speaker’s expression, gestures, and accent
at the face-to-face communication. Character-based
communication such as internet services has the potential to make readers misunderstand their real intention because of the limited information to express
their intention. Because of these situations, kaomoji
come into widespread use as communicating writer’s
implicit messages in the character-based communication.
kaomoji do exist over 100,000 kinds of their variations as previously noted. Nowadays, the numberof kaomoji increase day by day. The preferred situation is to keep renewing a large-scale knowledge
base kaomoji such as the proposed knowledge basein this paper, but it is distant idea because constructing large-scale knowledge base needs manual annotations. Therefore, we need the method that estimatesthe meaning that kaomoji expresses by extracting its
base form3 such as lemmatization of verbs and theother elements of it. For example, we can extract pos3 In this paper, we use this words (the base form) for kaomoji despite the existence of “the original form” or “infinitive”

Okumura N.
A Large Scale Knowledge Base Representing the Base Form of Kaomoji.
DOI: 10.5220/0006517002460252
In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KEOD 2017), pages 246-252
ISBN: 978-989-758-272-1
Copyright c 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Table 1: Rules and regulations to extract/make the base form of kaomoji.
kaomoji(ˆ ˆ)\(ˆ ˆ)/(’‘)

Original Form
(ˆˆ)
(ˆ ˆ)
(ˆ ˆ)
()

(T T)/˜˜˜

(T T)

(> -)
(;)

(> <), (- -)
(;;)

(ˆ ˆ;

(ˆ ˆ)

( (;)
)ˆˆ)

(), ()(ˆˆ)

Rules and regulations
Remove all spaces
Remove parts without borders, eyes, mouth and nose
Transform characters from two-byte characters to one-byte characters
If there are no corresponding one-byte characters, then the charactersremain in two-byte characters (in this case, the object is ω)
Do not transform characters from uppercase/lowercase to lowercase/uppercase (for instance, do not transform (T T) to (t t))
If kaomoji is asymmetry, then make it symmetry
If the eyes or some parts are covered with arms or something, then complement them using the other side of objective parts
If kaomoji have odd border, then complement it using the other side ofborder
If kaomoji have multiple faces, then extract each original form
If borders are asymmetry, then make border’s curve to outside

itive sentiment from kaomoji such as (ˆ_ˆ) because
we usually use this face as “smiling.” If we attach the
new element (*) to this kaomoji, we transform (ˆ_ˆ)
into (*ˆ_ˆ*). (*ˆ_ˆ*) has more positive sentimentthan (ˆ_ˆ). In fact, (*) has the role to emphasize
positive sentiment. By the same token, we can extract
negative sentiment from kaomoji such as () because
we usually use this face as “anger.” If we attach the
new element (*) to this kaomoji, we transform () into(**) . (**) has more negative sentiment than () .
In fact, (*) has the role to emphasize positive/negative
sentiment. We can guess the sentiment of (*ˆ_ˆ*) or(**) using the sentiment information of (ˆ_ˆ) or ()because we know the role of (*) whether we have or
do not have any information of (*ˆ_ˆ*) or (**) .
In this paper, we annotate about 40,000 kinds ofkaomoji collecting from websites4 . We extract about
3,000 kinds of the base form of kaomoji using annotated kaomoji. We also investigate whether N-grambased model can estimate the base form of kaomoji.
In experimental evaluations based on cosine similarity using N-gram based features and simple Skipgram based features, we show that the model can estimate the base form of kaomoji’ with an accuracy of
about 50%.

2

RELATED WORKThe research dealing with kaomoji is come across occasionally. The research, however, that focuses on
the relation: the base form - the derivative form as
our study is in the minority. Ptaszynski et al. proposed CAO: A Fully Automatic Emoticon Analysis
System to achieve kaomoji extraction with high accuracy(Ptaszynski et al., 2010b; Ptaszynski et al.,4 For example:http://www.kaomoji.sakura.ne.jp/

2010a). CAO system can manage over 10,000 kindsof kaomoji, and the system can extract over a few million of kaomoji. On the other hand, they do not focus
on kaomoji’s original form. CAO system only extracts
kaomoji from sentences using eye-mouth-eye triplet
in this scheme.
Bedrick et al. constructed a robust method to extract kaomoji from tweets on Twitter(Bedrick et al.,2012). They proposed the extraction method of kaomoji in consideration of symmetric property using
Probabilistic context-free grammar (PCFG). In ourstudy, we attach great importance to kaomoji’s symmetric property as their research.
Yamada et al. constructed the classification system for about 700 kaomoji using N-gram based feature extracted each kaomoji(Yamada et al., 2007).
This system classifies kaomoji to 8 emotions (smile,cry, anger, surprise, confuse, unsatisfied, anxiety, no
emotion) with an accuracy of 90%. They, however,focus on only global classification without putting
each element of kaomoji to the proof.
Tanaka et al. reported the classification system using K-means and Support Vector Machines (SVMs)
that classify kaomoji to 6 emotions (pleasure, sadness,anger, surprise, action, bitter smile) in the similar way
of Yamada’s research(Tanaka et al., 2005). These researches mainly focus on emotions that kaomoji have,but they do not use each element of kaomoji.
Kazama et al. construct a method to extract
kaomoij from Twitter5 (Kazama et al., 2016). Theirmethod to extract kaomoji focuses on Unicode blocks
and Unicode character properties. The method can
extract over 50 million kinds of kaomoji from Twitter(360 million tweets.) The method, however, cannot
5 This article is submitted to a Japanese conference andit is written in Japanese, however, their extraction methodof kaomoji marked high accuracy. We dare to introduce thisarticle here.

deal with kaomoji that have comments such as (**).6
The other research about kaomoji are the recommendation method of kaomoji(Urabe et al., 2013),
the method to extract kaomoji’s tense(Onishi andOkumura, 2014) and so on. “Science of Emoticons”(Ptaszynski et al., 2012) has a detailed knowledge of general kaomoji extraction method.

3

CONSTRUCTION OF
LARGE-SCALE KNOWLEDGE
BASE REPRESENTING THE
BASE FORM OF KAOMOJI

In this study, we annotate 70,106 kinds of kaomoji
that are collected from websites. Annotators add tags
to kaomoji’s each element using the rules in the following subsections. The terms to annotate kaomoji isfrom 1st October 2015 to 20th March 2016. Annotators are six persons including five males and a female.
As a result, we annotated 43,373 kaomoji out of all of
the collected kaomoji in the experimental period.

3.1 Definition of the Base Form of
Kaomoji
It is hard that we analyze the meaning of each elementof kaomoji because kaomoji have various elements.
Therefore, we define the base form of kaomoji such asstemming of verbs or lemmatization for easy handlingin natural language processing. The basic elements ofthe base form of kaomoji are the triplet (eye-moutheye: sometimes move away from mouth towards thenose) that Ptaszynski et al. proposed. Besides, we focus on the borders of kaomoji because almost kaomoji
have borders. Table 1 shows the rules and regulations
to extract/make the base form of kaomoji.
We extracted 3,110 base forms of kaomoji in thiswork. Table 2 shows the sample of the base form of
kaomoji in order of prevalence. The number of thebase forms that have only one derivative kaomoji is
1,173. Therefore, 1,973 kinds of the base form of kaomoji have two or more derivative kaomoji. We show
the details of each kaomoji’s element in the following
subsection.

Table 2: Examples of kaomoji’s original form.

The base form

Freq.

The base form

Freq.

(_)

1032

(ˆ-ˆ)

495

(-_-)

610

(00)

471

()

589

(w)

466

(__)

540

(o|o)

452

(>_<)

529

()

426

ear, forehead, border, arm, other body elements, caption, onomatopoeia, and the other expression. In thissection, we show the examples of these elements ofkaomoji.
3.2.1 Eyes
The smallest components of kaomoji are eyes. Therefore, this research pushes aside emoticons such as orz
or back shot that have no eyes because these emoticons do not show their faces. kaomoji sometimes havesigns of inequality as their eyes such as (>_<). Thesesigns indicate that eyes harmonized with arms. We
extract these signs as eyes in this study. Table 3 shows
the sample of eyes.
Table 3: Eyes: the elements of the base form of kaomoji.

kaomojiEyeskaomoji

(--)

- -

()

Eyes

3.2.2 Mouth, Nose
The next important elements of kaomoji are mouthand nose. The expressions dramatically increase withmouth or nose as the elements of kaomoji. On theother hand, a nose exists instead of mouth or a mouthharmonized with a nose. Therefore, we extract amouth and a nose as core elements of kaomoji’s original form by consensus of annotators. Table 4 shows
the samples of mouth and nose.
Table 4: Mouth and Nose: the second elements of the baseform of kaomoji.

3.2 Annotated Elements of Kaomoji
kaomoji
We annotated the collected kaomoji to extract the baseform of kaomoji. We also annotated each element that
falls under the category of eye, nose, mouth, cheek,
6 means “Hello.”

Mouth((( )))( ’’)-kaomojiNose(*ˆˆ*)

ˆˆ()@@

3.2.3 Borders

Table 7: The samples of arms.

Borders of kaomoji are made mostly of a parenthesis. In other words, a parenthesis is not an essential
element of kaomoji regarding discriminating kaomoji
with a concept of IDF(Inverse Document Frequency
in information retrieval or natural language processing). Borders, however, change the expression of kaomoji or the combination of borders express the movement of kaomoji. We extract borders as the element
parts of the base form of kaomoji because they havepossibilities to express a subtle sense. Table 5 shows
the samples of borders.
Table 5: Borders: the third elements of the base form of
kaomoji.

kaomoji

Borders

kaomoji

()

( )

_

Borders

kaomojiArms|O|kaomoji

Arms

( --)

3.2.6 Other Elements
Many various elements show something exclusive offace or body as the elements of kaomoji. These elements show the particular situation or emphasizing
emotions. Whether these parts belong to kaomoji remains a matter of debate in a similar way to arms. In
this paper, the existential reason of kaomoji is to resolve the issue of context in Japanese culture as weonce remarked in Section 1. Therefore, this study defines kaomoji as combined expression of faces and theother particular situations. Table 8 shows the sampleof the elements that shows particular situation. In thiscase, ˜ shows the situation: ”Do you have a cup ofJapanese tea?”.

3.2.4 Cheek, Ears, Forehead
The face has the other parts such as cheek, ears andforehead except for the elements as noted above. We
healthy people have these parts without any exceptions. On the other hand, kaomoji have these parts forthe rare occasion(frequency of these parts is low). We
extract the base form of kaomoji exclusive of theseparts because these parts have the role of decoratingkaomoji’s original form. Table 6 shows the samplesof cheek and ears.
Table 6: The samples of cheek and ears.
kaomojiCheekkaomojiEars(*ˆˆ*)* *Ł(() )Ł

Ł Ł

(;)

;

( )

3.2.5 Arms
The word “kaomoji has two japanese words: “kao”
and “moji.” The word “kao” means the “face” andthe word “moji” means the “characters or symbols”
in Japanese language under normal circumstances.
Whether arms, legs or some parts exclusive of theparts of face belong to kaomoji remains a matter of debate. In fact, there are many something like kaomoji
that have arms, legs, and so on. We extract arms as
the parts that add value to kaomoji in this study. Armsconsist of symmetry parts or one side parts. Therefore, we extract arms without considering symmetry
property. Table 7 shows the samples of arms.

Table 8: The other elements exclusive of face and arms.

kaomojiElements( -_-)˜˜

The manageability of these elements differs depending on the position that kaomoji have these elements on the left side or right side. For example, it isharder for CAO system or Bedrick’s PCFG to extract
sequences forward kaomoji than following kaomoji.
The reason is that kaomoji are used as the punctuationof sentences. We can extract the sequences following kaomoji using CAO system or Bedrick’s PCFG
because we usually use kaomoji at the end of sentences. On the other hand, it is unobvious where weshould return to extract sequences forward kaomoji.
Our knowledge base of kaomoji discriminates the sequences that are at the left side or the right side.
3.2.7 Caption, Onomatopoeia
Kaomoji have every character and symbol as the elements. These elements construct “sentence” or “onomatopoeia” in some instances. Sentences or onomatopoeia have detailed information such as emotions or situations. We extract this information fromkaomoji in a proactive way. For example, o(ˆ-ˆ)oshows the wonderful situation with onomatopoeia7.

7 means wonderful.

3.2.8 Other Expression
There are the characteristic elements of kaomoji otherthan described above. For example, (((()))))))
shows shuddering situation with repeated use. Repeated use of a certain element shows this expression.
We extract these repeated use element as the other expression of kaomoji.
We Japanese people regularly use two-byte characters in daily life. It is necessary to discriminate
between two-byte characters and one-byte characters.
For instance, (ˆˆ) and are similar looking faces because these kaomoji consist of same parts. These are,however, different faces in a strict sense because of
the former consist of one-byte characters, the latter
consist of two-byte characters. The latter looks likewell-rounded than the former. In this paper, we transform characters from two-byte characters to one-bytecharacters when we extract/make kaomoji’s originalform because of choking off the manifold.

4.1 Experiment to Estimate the Base
Form of Kaomoji using N-gram
ModelN-gram based features are widely used in natural language processing. We can use this simple model for
analyzing kaomoji. We can calculate the similarity
between kaomoji using N-gram based features by separating kaomoji. In this paper, we calculated the accuracy of estimating the base form of kaomoji using
N-gram based model (N=1 to 5)(Eq. 1). In the caseof Kaomoji that have multiple base forms, we countit a correct answer if one of the each base form is extracted.
Accuracy =num o f kaomoji estimated correctly(1)
num o f all annotated kaomoji (43, 373)

Cosine similarity is used to calculate similarity between kaomoji(Eq. 2).
Cosine(A, B) =4

EXPERIMENTS FOR
ESTIMATING THE BASE FORM
OF KAOMOJI

We can refer the base form of kaomoji and the otherelements of kaomoji using the large-scale knowledge
base of kaomoji constructed in this paper. There is,however, a high possibility of encountering unknown
kaomoji because of the number of kaomoji increase
day by day. We except UTF kaomoji despite the increase in kaomoji because we find out the UTF kaomoji that are outside our study. Figure 1 shows the
samples of UTF kaomoji.

A·B|A||B|(2)

Our method calculates similarity using Eq. 2 between input kaomoji and all of extracted/made thebase forms from large-scale knowledge base of kaomoji (Section 3.1). The base form of kaomoji thatscored the highest similarity is the estimated baseform. Table 9 shows the result of estimation using
N-gram based features.
Table 9: Accuracy of estimating the base form of kaomoji
using N-gram based single feature.

N-gram
1gram
2gram
3gram
4gram
5gram

Accuracy
0.244
0.386
0.440
0.336
0.019

Figure 1: kaomoji using UTF-8.

This paper investigates the method to estimate thebase form of kaomoji. If the method can estimate
the base form of kaomoji, then the method can also
estimate the information of kaomoji using the otherparts exclusive of the base form. In other words, themethod estimate emotions, situations, and gesturesusing the base form of kaomoji and the other elementsof it. We describe the method of estimating originalform using N-gram based features, simple Skip-gramfeatures, and the combination of these features in thefollowing section.

The highest accuracy is 0.440 (using Trigram). On
the other hand, the lowest accuracy is 0.019 (using
5-gram). 5-gram model seems to be useless for estimating original form. We investigate the estimationof kaomoji’s original form exclusive of 5-gram in thefollowing section.

4.2 Experiment to Estimate the Base
Form of Kaomoji using Simple
Skip-gram Model
Skip-gram is used in Word2Vec model proposed by
Mikolov et al(Mikolov et al., 2013). Skip-gram isthe model that use co-occurrence words in configured window size. If the window size equals 6,
then for example ”I go to school by bus.” is separated to ”I - go”, ”I - to”, ”go - bus” , and soon. In our study, we use more simple Skip-grammodel. We extend N-gram model to simple Skipgram model that does not use every combination ofco-occurrence words(characters) but only use skipping N words (characters). If the window size equals3(Skip 1 character model), then for example ”I go toschool by bus.” is separated to ”I - to”, ”go - school”,”to - by”, ”school - bus” by using our simple Skipgram model. We can change the expression of kaomoji by inserting a certain element. For instance, wetransform (ˆ_ˆ) into (*ˆ_ˆ*) by inserting (*). Simple Skip-gram model is effective about this situation.
Table 10 shows the result using naive Skip-gram(Skip
N characters N = 1 to 3). The highest accuracy is0.270(Skip 1 character model). However, the accuracy is lower than N-gram based model.

Table 11: Accuracy of estimating the base form of kaomoji
using combination of N-gram based features.

Combinations
1gram+2gram
1gram+3gram
1gram+4gram
2gram+3gram
2gram+4gram
3gram+4gram
1gram+2gram+3gram
1gram+2gram+4gram
1gram+3gram+4gram
2gram+3gram+4gram
1gram+2gram+3gram+4gram

Table 12: Accuracy of estimating the base form of kaomoji using combination of N-gram based features and naive
Skip-gram based features.

Combinations
2gram+Skip 1 character
3gram+Skip 1 character2gram+3gram+Skip 1 character

Table 10: Accuracy of estimating the base form of kaomoji
using simple Skip-gram based single feature.

naive Skip-gram
Skip 1 character
Skip 2 characters
Skip 3 characters

Accuracy
0.270
0.039
0.018

4.3 Experiment to Estimate the Base
Form of Kaomoji using the
Combination of N-gram and Simple
Skip-gram based Features
In the above experiment, we use only individual features such as only unigram, only trigram and so on.
We examine the multiple features to estimate the baseform of kaomoji. 5-gram model is removed in thisexperiment as previously noted.
Table 11 shows the result of combination
model(N-gram based features). The highest accuracy
is 0.433(bigram + trigram model). However, this accuracy is lower than using only trigram model despite
we form a hypothesis that the combination model improves the accuracy.
Table 12 shows the result of combination model(N-gram and naive Skip-gram model). The highest
accuracy is 0.489 (bigram + trigram + Skip 1 character model) with contrary to expectations.
Figure 2 shows each the highest accuracy of estimation using N-gram and simple Skip-gram based
features.

Accuracy
0.381
0.406
0.339
0.433
0.410
0.425
0.422
0.401
0.414
0.427
0.431

5

Accuracy
0.470
0.404
0.489

CONCLUSION

In this paper, we annotated kaomoji and constructeda large-scale knowledge base of kaomoji. The total
number of annotated kaomoji is 43,373 out of our collected kaomoij. As a result, we extracted 3,110 kindsof the base form of kaomoji. In experimental evaluations, we achieved 0.489 accuracy of estimating thebase form kaomoji using bigram + trigram + Skipping
1 character model. Ih the case of random estimation,
the maximum probability is 1,032 / 43,373 (=0.024)
that Table 2 shows. We consider that our experiments
are efficient for estimating the base form of kaomoij.
Accuracy
60%
50%

48.9%
44.0%

43.3%

3gram

2gram+3gram

40%
30%
20%
10%
0%
2gram+3gram+Skip 1 character

Figure 2: Result of experiments using N-gram and simple
Skip-gram based fearures.

As future works, we will finish annotating remaining kaomoji. We will attempt to construct classification method using deep learning tools such as
Chainer, TensorFlow, and so on to improve the accuracy of estimation (c.f. emoji2vec(Eisner et al.,2016)). In addition, we have to annotate kaomoji’s
emotions based on Plutchik model(Plutchik, 1980).
Because we do not extract emotions that kaomoji
shows in this paper.

ACKNOWLEDGEMENT
This work was supported by JSPS KAKENHI Grant
Number 15K21592.

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