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 to

classify 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 another

feature 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 large-

scale 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 that

the model can estimate the base form of kaomoji with an accuracy of about 50%.

1 INTRODUCTION

How can we understand our real intentions, emo-

tions or feelings each other using only written words

in computer-mediated media such as Twitter

, Face-

book

or something like them? Can we send our ges-

tures using only characters? One of the solutions is

kaomoji. Kaomoji is a typical sequence that can send

writer’s expression, sign, emotion, and so on to the

reader with similar uses as an emoticon, a pictogram,

a smiley, and a stamp using only written words. Kao-

moji 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 com-

municating writer’s emotions, gestures and so on in

Japan? Because Japanese culture is the most high-

context culture all over the world. People living in

the high-context culture can communicate with each

other without expressing their opinions clearly on

a 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 cul-

ture (for example, English-speaking people, German-

speaking people, and so on) communicate with each

other using a clear explanation because they do not

https://twitter.com

http://www.facebook.com

have a cultural context in common. It is hard that

people do communication with each other at the

character-based communication on the internet when

using high-context language such as Japanese. People

can estimate the intension of speaker’s utterance with

observing speaker’s expression, gestures, and accent

at the face-to-face communication. Character-based

communication such as internet services has the po-

tential to make readers misunderstand their real in-

tention 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 communica-

tion.

kaomoji do exist over 100,000 kinds of their vari-

ations as previously noted. Nowadays, the number

of kaomoji increase day by day. The preferred sit-

uation is to keep renewing a large-scale knowledge

base kaomoji such as the proposed knowledge base

in this paper, but it is distant idea because construct-

ing large-scale knowledge base needs manual annota-

tions. Therefore, we need the method that estimates

the meaning that kaomoji expresses by extracting its

base form

such as lemmatization of verbs and the

other elements of it. For example, we can extract pos-

In this paper, we use this words (the base form) for kao-

moji despite the existence of “the original form” or “inﬁni-

tive”

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

 2017 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

Table 1: Rules and regulations to extract/make the base form of kaomoji.

kaomoji Original Form Rules and regulations

(ˆ ˆ) (ˆˆ)

Remove all spaces

(ˆ ˆ)/ (ˆ ˆ)

Remove parts without borders, eyes, mouth and nose

(ˆ ˆ)

Transform characters from two-byte characters to one-byte characters

(’‘) ()

If there are no corresponding one-byte characters, then the characters

remain in two-byte characters (in this case, the object is ω)

(T T)/˜˜˜ (T T)

Do not transform characters from uppercase/lowercase to lower-

case/uppercase (for instance, do not transform

(T T)

(t t)

)

(

-) (

> <

)

(- -)

If kaomoji is asymmetry, then make it symmetry

(;) (;;)

If the eyes or some parts are covered with arms or something, then com-

plement them using the other side of objective parts

(ˆ ˆ; (ˆ ˆ)

If kaomoji have odd border, then complement it using the other side of

border

( (;) ()

()

If kaomoji have multiple faces, then extract each original form

)ˆˆ) (ˆˆ)

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*)

has more positive sentiment

than

(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*)

(**)

using the sentiment information of

(ˆ_ˆ)

()

because we know the role of (

) whether we have or

do not have any information of

(*ˆ_ˆ*)

(**)

In this paper, we annotate about 40,000 kinds of

kaomoji collecting from websites

. We extract about

3,000 kinds of the base form of kaomoji using anno-

tated kaomoji. We also investigate whether N-gram

based model can estimate the base form of kaomoji.

In experimental evaluations based on cosine similar-

ity using N-gram based features and simple Skip-

gram based features, we show that the model can es-

timate the base form of kaomoji’ with an accuracy of

about 50%.

2 RELATED WORK

The research dealing with kaomoji is come across oc-

casionally. The research, however, that focuses on

the relation: the base form - the derivative form as

our study is in the minority. Ptaszynski et al. pro-

posed CAO: A Fully Automatic Emoticon Analysis

System to achieve kaomoji extraction with high ac-

curacy(Ptaszynski et al., 2010b; Ptaszynski et al.,

For example:http://www.kaomoji.sakura.ne.jp/

2010a). CAO system can manage over 10,000 kinds

of kaomoji, and the system can extract over a few mil-

lion 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 ex-

tract kaomoji from tweets on Twitter(Bedrick et al.,

2012). They proposed the extraction method of kao-

moji in consideration of symmetric property using

Probabilistic context-free grammar (PCFG). In our

study, we attach great importance to kaomoji’s sym-

metric property as their research.

Yamada et al. constructed the classiﬁcation sys-

tem for about 700 kaomoji using N-gram based fea-

ture 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 us-

ing 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 re-

searches 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 Twitter

(Kazama et al., 2016). Their

method 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

This article is submitted to a Japanese conference and

it is written in Japanese, however, their extraction method

of kaomoji marked high accuracy. We dare to introduce this

article here.

deal with kaomoji that have comments such as

(**)

The other research about kaomoji are the recom-

mendation method of kaomoji(Urabe et al., 2013),

the method to extract kaomoji’s tense(Onishi and

Okumura, 2014) and so on. “Science of Emoti-

cons”(Ptaszynski et al., 2012) has a detailed knowl-

edge 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 fol-

lowing subsections. The terms to annotate kaomoji is

from 1st October 2015 to 20th March 2016. Annota-

tors 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 element

of kaomoji because kaomoji have various elements.

Therefore, we deﬁne the base form of kaomoji such as

stemming of verbs or lemmatization for easy handling

in natural language processing. The basic elements of

the base form of kaomoji are the triplet (eye-mouth-

eye: sometimes move away from mouth towards the

nose) that Ptaszynski et al. proposed. Besides, we fo-

cus 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 this

work. Table 2 shows the sample of the base form of

kaomoji in order of prevalence. The number of the

base forms that have only one derivative kaomoji is

1,173. Therefore, 1,973 kinds of the base form of kao-

moji have two or more derivative kaomoji. We show

the details of each kaomoji’s element in the following

subsection.

3.2 Annotated Elements of Kaomoji

We annotated the collected kaomoji to extract the base

form of kaomoji. We also annotated each element that

falls under the category of eye, nose, mouth, cheek,

means “Hello.”

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, cap-

tion, onomatopoeia, and the other expression. In this

section, we show the examples of these elements of

kaomoji.

3.2.1 Eyes

The smallest components of kaomoji are eyes. There-

fore, this research pushes aside emoticons such as

orz

or back shot that have no eyes because these emoti-

cons do not show their faces. kaomoji sometimes have

signs of inequality as their eyes such as

(>_<)

. These

signs 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.

kaomoji Eyes kaomoji Eyes

(--) - - ()

3.2.2 Mouth, Nose

The next important elements of kaomoji are mouth

and nose. The expressions dramatically increase with

mouth or nose as the elements of kaomoji. On the

other hand, a nose exists instead of mouth or a mouth

harmonized with a nose. Therefore, we extract a

mouth and a nose as core elements of kaomoji’s orig-

inal form by consensus of annotators. Table 4 shows

the samples of mouth and nose.

Table 4: Mouth and Nose: the second elements of the base

form of kaomoji.

kaomoji Mouth kaomoji Nose

((( ))) (*ˆˆ*) ˆˆ

( ’’) - () @@

3.2.3 Borders

Borders of kaomoji are made mostly of a parenthe-

sis. 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 process-

ing). Borders, however,change the expression of kao-

moji or the combination of borders express the move-

ment of kaomoji. We extract borders as the element

parts of the base form of kaomoji because they have

possibilities 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

() ( ) _

3.2.4 Cheek, Ears, Forehead

The face has the other parts such as cheek, ears and

forehead except for the elements as noted above. We

healthy people have these parts without any excep-

tions. On the other hand, kaomoji have these parts for

the rare occasion(frequency of these parts is low). We

extract the base form of kaomoji exclusive of these

parts because these parts have the role of decorating

kaomoji’s original form. Table 6 shows the samples

of cheek and ears.

Table 6: The samples of cheek and ears.

kaomoji Cheek kaomoji Ears

(*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” and

the word “moji” means the “characters or symbols”

in Japanese language under normal circumstances.

Whether arms, legs or some parts exclusive of the

parts of face belong to kaomoji remains a matter of de-

bate. 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. Arms

consist of symmetry parts or one side parts. There-

fore, we extract arms without considering symmetry

property. Table 7 shows the samples of arms.

Table 7: The samples of arms.

kaomoji Arms kaomoji Arms

|O| ( --)

3.2.6 Other Elements

Many various elements show something exclusive of

face or body as the elements of kaomoji. These el-

ements show the particular situation or emphasizing

emotions. Whether these parts belong to kaomoji re-

mains a matter of debate in a similar way to arms. In

this paper, the existential reason of kaomoji is to re-

solve the issue of context in Japanese culture as we

once remarked in Section 1. Therefore, this study de-

ﬁnes kaomoji as combined expression of faces and the

other particular situations. Table 8 shows the sample

of the elements that shows particular situation. In this

case,

shows the situation: ”Do you have a cup of

Japanese tea?”.

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

kaomoji Elements

( -_-)˜ ˜

The manageability of these elements differs de-

pending on the position that kaomoji have these ele-

ments on the left side or right side. For example, it is

harder for CAO system or Bedrick’s PCFG to extract

sequences forward kaomoji than following kaomoji.

The reason is that kaomoji are used as the punctuation

of sentences. We can extract the sequences follow-

ing kaomoji using CAO system or Bedrick’s PCFG

because we usually use kaomoji at the end of sen-

tences. On the other hand, it is unobvious where we

should return to extract sequences forward kaomoji.

Our knowledge base of kaomoji discriminates the se-

quences that are at the left side or the right side.

3.2.7 Caption, Onomatopoeia

Kaomoji have every character and symbol as the ele-

ments. These elements construct “sentence” or “ono-

matopoeia” in some instances. Sentences or ono-

matopoeia have detailed information such as emo-

tions or situations. We extract this information from

kaomoji in a proactive way. For example,

o(ˆ-ˆ)o

shows the wonderful situation with onomatopoeia

means wonderful.

3.2.8 Other Expression

There are the characteristic elements of kaomoji other

than described above. For example,

(((()))))))

shows shuddering situation with repeated use. Re-

peated use of a certain element shows this expression.

We extract these repeated use element as the other ex-

pression of kaomoji.

We Japanese people regularly use two-byte char-

acters in daily life. It is necessary to discriminate

between two-byte characters and one-byte characters.

For instance,

(ˆˆ)

and are similar looking faces be-

cause 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 like

well-rounded than the former. In this paper, we trans-

form characters from two-byte characters to one-byte

characters when we extract/make kaomoji’s original

form because of choking off the manifold.

4 EXPERIMENTS FOR

ESTIMATING THE BASE FORM

OF KAOMOJI

We can refer the base form of kaomoji and the other

elements 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 in-

crease in kaomoji because we ﬁnd out the UTF kao-

moji that are outside our study. Figure 1 shows the

samples of UTF kaomoji.

Figure 1: kaomoji using UTF-8.

This paper investigates the method to estimate the

base 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 other

parts exclusive of the base form. In other words, the

method estimate emotions, situations, and gestures

using the base form of kaomoji and the other elements

of it. We describe the method of estimating original

form using N-gram based features, simple Skip-gram

features, and the combination of these features in the

following section.

4.1 Experiment to Estimate the Base

Form of Kaomoji using N-gram

Model

N-gram based features are widely used in natural lan-

guage processing. We can use this simple model for

analyzing kaomoji. We can calculate the similarity

between kaomoji using N-gram based features by sep-

arating kaomoji. In this paper, we calculated the ac-

curacy of estimating the base form of kaomoji using

N-gram based model (N=1 to 5)(Eq. 1). In the case

of Kaomoji that have multiple base forms, we count

it a correct answer if one of the each base form is ex-

tracted.

Accuracy =

num of kaomoji estimated correctly

num of all annotated kaomoji (43, 373)

(1)

Cosine similarity is used to calculate similarity be-

tween kaomoji(Eq. 2).

Cosine(A, B) =

A· B

|A||B|

(2)

Our method calculates similarity using Eq. 2 be-

tween input kaomoji and all of extracted/made the

base forms from large-scale knowledge base of kao-

moji (Section 3.1). The base form of kaomoji that

scored the highest similarity is the estimated base

form. 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 Accuracy

1gram 0.244

2gram 0.386

3gram 0.440

4gram 0.336

5gram 0.019

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 esti-

mating original form. We investigate the estimation

of kaomoji’s original form exclusive of 5-gram in the

following 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 conﬁg-

ured window size. If the window size equals 6,

then for example ”I go to school by bus.” is sep-

arated to ”I - go”, ”I - to”, ”go - bus” , and so

on. In our study, we use more simple Skip-gram

model. We extend N-gram model to simple Skip-

gram model that does not use every combination of

co-occurrence words(characters) but only use skip-

ping N words (characters). If the window size equals

3(Skip 1 character model), then for example ”I go to

school by bus.” is separated to ”I - to”, ”go - school”,

”to - by”, ”school - bus” by using our simple Skip-

gram model. We can change the expression of kao-

moji by inserting a certain element. For instance, we

transform

(ˆ_ˆ)

into

(*ˆ_ˆ*)

by inserting (

). Sim-

ple 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 is

0.270(Skip 1 character model). However, the accu-

racy is lower than N-gram based model.

Table 10: Accuracy of estimating the base form of kaomoji

using simple Skip-gram based single feature.

naive Skip-gram Accuracy

Skip 1 character 0.270

Skip 2 characters 0.039

Skip 3 characters 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 fea-

tures such as only unigram, only trigram and so on.

We examine the multiple features to estimate the base

form of kaomoji. 5-gram model is removed in this

experiment 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 ac-

curacy is lower than using only trigram model despite

we form a hypothesis that the combination model im-

proves 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 charac-

ter model) with contrary to expectations.

Figure 2 shows each the highest accuracy of es-

timation using N-gram and simple Skip-gram based

features.

Table 11: Accuracy of estimating the base form of kaomoji

using combination of N-gram based features.

Combinations Accuracy

1gram+2gram 0.381

1gram+3gram 0.406

1gram+4gram 0.339

2gram+3gram 0.433

2gram+4gram 0.410

3gram+4gram 0.425

1gram+2gram+3gram 0.422

1gram+2gram+4gram 0.401

1gram+3gram+4gram 0.414

2gram+3gram+4gram 0.427

1gram+2gram+3gram+4gram 0.431

Table 12: Accuracy of estimating the base form of kao-

moji using combination of N-gram based features and naive

Skip-gram based features.

Combinations Accuracy

2gram+Skip 1 character 0.470

3gram+Skip 1 character 0.404

2gram+3gram+Skip 1 character 0.489

5 CONCLUSION

In this paper, we annotated kaomoji and constructed

a large-scale knowledge base of kaomoji. The total

number of annotated kaomoji is 43,373 out of our col-

lected kaomoij. As a result, we extracted 3,110 kinds

of the base form of kaomoji. In experimental evalu-

ations, we achieved 0.489 accuracy of estimating the

base 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.

44.0%

43.3%

48.9%

10%

20%

30%

40%

50%

60%

3gram 2gram+3gram 2gram+3gram

+Skip 1 character

Accuracy

Figure 2: Result of experiments using N-gram and simple

Skip-gram based fearures.

As future works, we will ﬁnish annotating re-

maining kaomoji. We will attempt to construct clas-

siﬁcation method using deep learning tools such as

Chainer, TensorFlow, and so on to improve the ac-

curacy 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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