Creating Character Networks from Comics
using Frames and Words in Balloons
Harumi Murakami

Yusuke Nagaoka

Ryota Kyogoku

Graduate School of Engineering
Osaka City University
3-3-138, Sugimoto, Sumiyoshi,
Osaka 558–8585 Japan
Email: harumi@osaka-cu.ac.jp

Graduate School for Creative Cities
Osaka City University
3-3-138, Sugimoto, Sumiyoshi,
Osaka 558–8585 Japan

Graduate School for Creative Cities
Osaka City University
3-3-138, Sugimoto, Sumiyoshi,
Osaka 558–8585 Japan

Abstract—We create character networks from comics using
the frames and the words in balloons to find and understandthem. First, we build a dataset that contains a comic’s charactersand its words in balloons separated by frames. Second, we extract
the semantic relations among characters using the words in theballoons and the frames. Third, we develop character networks
by identifying the important characters with their relatedness
and add semantic relations to the networks. We implemented
our approach and successfully built character networks fromfour popular Japanese comics and evaluated our kinship relation
extraction algorithms using them.
Keywords—comics, character networks, character connections,frames, words in balloons

I.

I NTRODUCTION

In recent years, the popularity of Japanese manga (comics)
has increased worldwide, and a large amount of it is being
published. Japanese manga is usually serialized in magazinesand later compiled in books. Some manga resembles longepics. Since finding a chapter, a specific volume, or evena particular manga itself from a large amount of manga isdifficult, we investigate how to find a chapter or a volume ofa particular manga.
Character networks, which include character charts or
character connections, are often created to introduce the contents of such multimedia as movies, anime, and TV dramas
to help people understand the characters and complicatedstories. Character networks are also useful for finding andunderstanding comics. However, it remains unclear how tocreate character networks from comics.
This research creates character networks from comics to
help users find and understand their contents.
In our previous work [1], we proposed a method of creating
character networks from comics using the frequencies ofcharacters and their co-occurrences by referring to frames. Although that pioneering work highlighted the potential of suchan approach, we did not implement algorithms for creating
character networks or present any for extracting the semantics
among characters using texts.
In this research that uses the words included in balloons,
we enhance our previous work and create character networks
with semantic relations. In addition, we revise our algorithm

that creates character networks and present a new version ofit.
In what follows, related work is shown in Section 2. We
explain our approach’s overview and our algorithms in Section
3. Experimental results using four popular Japanese comics areshown in Section 4.
II.

R ELATED W ORK

To the best of our knowledge, our previous work [1] with
its original idea is the only research that created character
networks from comics. In that work, we assumed that characters who appear in identical or in the next frames are related
and created character networks using the frequencies of thecharacters and their co-occurrences in identical or the next
frames. We used a volume of a very famous Japanese comic(Dragon Ball) for that previous experiment. In this new work,
we extend our previous work by modifying our algorithm
that creates character networks and adding kinship relations
to our character networks. We implemented and evaluatedour algorithms using four different popular Japanese comics:
Naruto, Dragon Ball (different volumes from those used in theprevious work), Boys Over Flowers, and Space Brothers.
Some research has created character networks from literarytexts such as novels. We briefly discuss such research by
selecting examples of them.
Elson et al. [2] extracted social networks from 19th century
British novels and serials for literary studies. They foundinstances of quoted speech, attributed each quote to a character,and identified when certain characters are having a conversation. This work is related to ours in the sense that they createcharacter networks based on the frequency of the charactersand the interaction-based-weight between them. However, they
did not display any semantic relations among characters.
Lee and Yeung [3] created people (characters) and place
networks from literary texts. They translated the first five booksof the Hebrew bible into English and used them for theirevaluation. They used not only dialogue interaction but also
explanatory texts as weight relations between characters. They
did not display any relations between characters.
Nishihara [4] extracted the relations among characters fromJapanese novels and created character networks. His work is
the most closely related to ours because he deals with Japaneseand creates character networks with semantic relations suchas kinship. The main difference is that he deals with novels
instead of comics. Another major difference for connection
generation is that he does not specify characters to display; wechoose main characters with related characters to summarize
a comic’s content in one diagram. As for the extracting
relation algorithms, he mainly extracts useful information fromexplanatory texts using pattern-matching. For example, the “P1
is a sister of P2” template extracts a P1-sister-P2 relation.
He defined 53 relations among characters including kinshiprelations. Since comics generally have few explanations intheir texts, we treat the texts in balloons near the charactersas “dialogs” and extract kinship relations from the above
information.
Kokkinakis et al. [5] identified personal relationships between main characters in 19th century Swedish fiction using
information guided by named entities. They used a relationshipvocabulary, which includes kinship relations, for labeling andextracted sentences with co-occurring pairs of person namedentities and labeled the extracted pairs of person entities using
window sizes. One way is a window size of 1-3 tokens thatuses pattern matching templates with lexical units from theresources. Another way is with a window size of 4-10 tokens
to measure the context similarity between the extracted pairsof person entities. Our way is different from the work of
Kokkinakis et al. in a sense that we treat the texts in theballoons near the characters as dialogs, as we described in
the previous paragraph.
Some research created character networks from other media. For example, Goto et al. [6] made character charts from
EPG texts that introduce movies. Ogasawara et al. [7] extracted
people from broadcast videos to construct a human correlationgraph and examined both text and image processing. They did
not implement their graphs.
Spysee2 (http://spysee2.jp/), whose original algorithm isbased on Matsuo et al. [8], extracted person information fromthe web and displays social networks. Famous manga characternames such as Naruto can be input. Users can explore thesocial networks created from the information about Naruto on
the web, not from media contents. Even though this method
is useful for well-known comics, it is inadequate to express
the character networks of such designated units as chapters orvolumes based on media contents.
III.

M ETHOD

A. Overview
This research creates character networks from comics using
the frames and the words in balloons to find and understandthem. First, we built a dataset that contains a comic’s charactersand its words in balloons separated by frames. Second, we
extracted the semantic relations among characters using thewords in the balloons and the frames. Third, we developed
character networks by identifying the important characters by
their relatedness and added semantic relations to the networks.
The main feature of this research is creating character
networks from comics using the frequencies of characters andtheir co-occurrences by referring to the frames and identifying
the relations between characters using the words in balloons.

&RPLF

0DVWHUSXSLO)DWKHU&KLOG
OLNH

Fig. 1.

&UHDWLQJ'DWDVHW'DWDVHW*HQHUDWLQJ&KDUDFWHU
1HWZRUNV([WUDFWLQJ
6HPDQWLF
5HODWLRQV

Overview of our approach.

.DNDVKL +DWDNH
1DJDWR'DGKH
FDPH&KRML $NLPLFKL 'DG KHFDPH.DNDVKL+DWDNH&KR]D $NLPLFKL&KR]D $NLPLFKL&KRML $NLPLFKL

1DJDWR

D &RPLF

Fig. 2.

E 'DWDVHWExample of creating a dataset.

No current work creates character networks from comics
except for our previous work.
An overview of our approach is shown in Figure 1. In thefollowing, we illustrate our approach using the famous comic
Naruto Vols. 46-50. Note that the examples presented in thispaper were translated into English for publication.
B. Creating Dataset
We manually create a comic dataset comprised of sets ofa character and his/her words in balloons that are divided byframes.
For example, as in Figure 2, a dataset is created from a
comic. Note that Japanese comics flow from top to bottom
and right to left.
C. Extracting Semantic Relations among Characters
We extract the semantic relations among characters using
a heuristic method based on matching words and dictionaries.
As an initial step, we focus on kinship by capturing calls(such as father or mother). For example, if the words inballoons contain the word father and satisfy certain conditions,the speaker is a child. We assume that the recipient of this callwill be characters in the same or in the next frames.
1) Extracting Relations using Kinship Dictionary: We illustrate our method with the following child-father relation.
Step 1. Matching whether a term in the dictionary (e.g.,
dad, father) appears in the words.
Table I shows our dictionary with six family relations. Notethat in Japan, although younger siblings use brother or sisterto denote their older brother/sisters, older siblings seldom use
these terms for younger brother/sisters.
Step 2. When a term exists in words, if the previous term
is not no (means “of”) and the following term is not a particle,
we assume that the speaker is talking to his father.

TABLE I.
Relation (abbreviation)
child-father (C-F)
child-mother (C-M)
person-older brother (P-OB)
person-older sister (P-OS)
grandchild-grandfather (GC-GF)
grandchild-grandfather (GC-GM)

TABLE II.
No.
1
2
3
...
5
...
13
14
...
17
...
19

K INSHIP DICTIONARY
Sample terms
dad, father
mom, motherbrothersister
grandpa
grandma

From
child
child
personpersongrandchildgrandchild

TABLE III.
To
fathermotherolder brother
older sister
grandfather
grandmotherNo.
1

Character 2
NagatoKakashi Hatake
Choza Akimichi...
Fukasaku...
Akamaru
Tsume Inuzuka
...
Shima
...
Nagato’s mother

Relationchild-father
child-father
child-father...
child-father...
child-motherchild-mother...
child-mother...
child-mother

Score
3
2
4
...
4
...
2
2
..
4
...
1

Step 2.1 When there are other characters in the same frame
as the speaker, a point is given to the relations between the
speaker and the other characters in the frame.
Step 2.2 When there is only one character (speaker) in theframe, a point is given to the relations between the speaker
and other characters in the next (before or after) frames.
In the example of Figure 2, since Dad is included in thechild-father dictionary and condition Step 2 is also satisfied,relations are created between Choji Akimichi and Nagato,
Kakashi Hatake, and Choza Akimichi. In this case, Choji
Akimichi who said “Dad” becomes a child and Nagato,
Kakashi Hatake, and Choza Akimichi become fathers.
Table II shows a part of the extracted relations from Naruto
Vols. 46-50. Twelve child-father and seven child-mother relations were extracted.
2) Identifying Spouse and Modifying Relations: In Japan,
married couples with children often call each other “father” or“mother” as if they are nicknames. We also use this custom
for our work. If two characters call each other “father” and“mother,” they are identified as a married couple, a new married couple relation (in this paper, a wife-husband relation) is
created and the previous child-father and child-mother relations
are deleted. In other words, if character 1 and character 2 havechild-father relation points and character 2 and character 1 havechild-mother relation points, character 1 is a wife and character2 is a husband.
In the example in Table II, a wife-husband relation between
Shima and Fukasaku is created. The original two Shima
between Fukasaku (child-father and child-mother) relations aredeleted.
3) Modifying Child-Parent Relations: In general, a person
has just one father and one mother. We select the most likely
child-parent relations based on the candidate relations.
If a person has multiple child-father or child-mother relations, the relations are selected with maximum scores. If

5Fukasaku

6
7

Shima
Fukasaku

2
3

E XTRACTED RELATIONS USING KINSHIP DICTIONARY
Character 1
Choji Akimichi
Choji Akimichi
Choji Akimichi
...
Shima
...
Kiba Inuzuka
Kiba Inuzuka...
Fukasaku...
Nagato4

Character 1
Choji
AkimichiKakashi
HatakeNarutoUzumaki
Kiba Inuzuka

R ESULTS OF EXTRACTED RELATIONS

Character 2
Choza
Akimichi
SakumoHatakeMinatoNamikaze
Tsume
InuzukaNarutoUzumaki
Fukasaku
MinatoNamikaze

Relationchild-father

Score
4

Judge
✓

child-father

3✓

child-father

3✓

child-mother2✓

child-mother2

× errorwife-husband
grandchildgrandfather––✓
× error(with
inference
only)

Precision: 5/6 = 0.833 (without inference); 5/7 = 0.714 (with inference)

multiple candidates remain, we perform pattern-matching on
the character names and select the relation that has the mostsimilar name. If the similarity is identical, all of the candidates
are deleted.
Now in our examples, we selected Choji Akimichi and
Choza Akimichi as the child-father since its score is maximum(4), and other two child-father candidate relations are deleted.
There are four child-mother candidate relations whosechild is Kiba Inuzuka. First, Kiba Inuzuka-Katsuyu is deleted
because its score is 1 (not maximum). Next, name similarity
chooses Tsume Kinuzuka as Kiba Inuzuka’s mother.
We obtained the result relations shown in No. 1-6 in Table
III.
4) Modifying Relations using Thresholds: Since the main
and related characters appear many times, many (relatively)
unimportant relation candidates might be extracted. We set
thresholds to delete such relations.
First, we classified the characters into five groups by theircumulative appearance rates. In our Naruto example, there
are a total of 8,089 character appearances. Naruto Uzumaki(the main character) appears 1,210 times, and his appearance
rate is 0.15 (1,210/8,089). Nagato appears 1,200 times, andhis cumulative appearance rate is (1,210+1,200)/8,089 = 0.30.
These two characters are classified in group 1 (see Table IV).
Next we set thresholds for each group using Eq. (1). For
our Naruto example, Num = 4/2 = 2. The threshold for group
1 is 2, for group 2 it is 1, and for groups 3 to 5, it is 0.
N um =

max(score).
2

T hgroup = N um − (group − 1).
Here group is a group number (i.e., 1, 2, 3, 4 or 5).
If T hgroup is under 0, then set zero to T hgroup :T hgroup ={round up to pos integer (T hgroup > 0). (1)0(T hgroup ≤ 0)

TABLE IV.
Rank
1
2
3
4
5
5
7
8
9
10
11
12
13
...
26
27
28
28
30
31
32
...

R ANKING OF C HARACTER APPEARANCES

Character
Naruto Uzumaki
NagatoSakura Haruno
Sasuke Uchiha
Kakashi Hatake
A
Tobi
Sai
Onoki
Danzo
Fukasaku
Konan
Killer Bee...
YahikoJugo
Jiraiya
YamatoShizuneMinato Namikaze
Shikamaru Nara...

TABLE V.

Appearance
1,210
1,200
264
253
251
251
202
172
167
154
152
141
140
...
88
86
84
84
83
81
76
...

Group
1
1
2
2
2
2
3
3
3
3
3
3
3
...
4
4
4
4
4
4
4
...

Display✓✓✓

Table III shows the final result for our Naruto example.
We extracted six relations without inference and added one
relation by the inference process. Five of six relations (without
inference) are correct and out of seven (with inference) arecorrect.

✓

E. Creating Character Networks
✓
✓
✓
✓
✓
✓
✓

I NFERENCE RULES

A father of a father is a grandfather.
A mother of a mother is a grandmother.
A father of a mother is a grandfather.
A mother of a mother is a grandmother.
People who share a father are brothers or sisters.
People who share a mother are brothers or sisters.
A brother/sister of a father is an uncle/aunt.
A brother/sister of a mother is an uncle/aunt.
Brothers or sisters have identical parents.

Third, we set thresholds for each relation using the minimal
character threshold:T hcharacter = T hgroup

T hrelationAB = min(T hcharacterA , T hcharacterB ).
For example, for the Naruto Uzumaki-Minato Namikaze
relation, the threshold for Naruto Uzumaki is 2 and for Minato
Namikaze is 0, and the threshold for their relation becomes 0.
Since the score of this relation (3) exceeds the threshold (0),it is not deleted.
In our example, since the scores of all the examples exceed
their threshold, no relation is deleted in Table III.
D. Adding Relations using Inference
Family inference is a typical example for artificial intelligence. Such relations as grandparent-child can be definedusing basic parent-child or spouse relations. Table V shows
the inference rules used in this research.
With the above rules, person-uncle and person-aunt relations can be generated without the kinship dictionary.
In our Naruto example, from the Naruto Uzumaki-Minato
Namikaze child-father relation and the Fukasaku-Naruto Uzumaki child-mother relation, a Fukasaku-Minato Namikaze
grandchild-grandfather relation can be created.

Various networks can be drawn, and we present an
overview network that summarizes the contents of the comics
in a single diagram.
We assume that critical characters appear more frequently
than other characters. We select the three most frequentcharacters first and then display three related characters forthem. Finally, lines and semantic relations are drawn among
characters. Creating a character network algorithm is described
below.
Step 1. Three “main” characters whose appearance is in
the top three are displayed up, down-left, and down-right.
Step 2. Three other related characters whose relatedness(Eq. (2), see below) is in the top three for the three main
characters (except for the main characters) are selected. Theseother characters are displayed next to the three main characters.
When they are related to more than one main character, they
are displayed between the related main characters.
Step 3. Lines are connected from the main characters to
the related displayed characters (including main and relatedcharacters).
Step 4. The extracted relation names (e.g., child-father) aredescribed next to the relations.
Calculating the relatedness among characters, which was
originally presented in our previous work [1], is redefined asfollows.
The relatedness score between characters ci and cj is
calculated as the summation of their related scores using the
co-appearance inside the frames and the scores using theappearance in the next frames, as shown in Eq. (2).
{1/nRinside (ci , cj ) =0.25(n = 1, 2, 3, 4)(n ≥ 5)where n is the number of different characters from thedesignated character inside a frame.
{
Rnext (ci , cj ) =0.5/n (n = 1, 2, 3, 4)
0.25(n ≥ 5)where n is the number of different characters from thedesignated character in the next frame.

R(ci , cj ) =

n
∑
k=1

Rinside (ci , cj ) +n−1∑

Rnext (ci , cj ).

(2)

k=1

Figure 3 shows the character network that is automaticallycreated from our Naruto example. Naruto Uzumaki (mainTABLE VI.
Rank
1
2
3
4
5
6
6
8
9
10
11
12
13
14
15
16
17
...
52
...
62
...

R ANKING OF CHARACTER RELATEDNESSCharacter 1
Naruto Uzumaki
Naruto Uzumaki
NagatoNaruto Uzumaki
Naruto Uzumaki
Killer Bee
Naruto Uzumaki
NagatoNagatoNaruto Uzumaki
Naruto Uzumaki
Sasuke Uchiha
Naruto Uzumaki
Karui
Sakura Haruno
Sasuke Uchiha
Nagato...
Kakashi Hatake...
Sakura Haruno...

Character 2
NagatoSakura Haruno
Konan
Fukasaku
Minato Namikaze
Kisame Hoshigaki
Kakashi Hatake
YasukeJiraiya
Katsuyu
Sai
A
Karui
Omoi
Sai
Karin
Kakashi Hatake...
Sakura Haruno...
Shikamaru Nara
...

Score
442.292
98.65891.40091.34285.68383.91766.62562.85858.76755.29252.56751.26751.21747.30843.35837.18335.433...
17.550...
14.558...

TABLE VII.

Display
✓
✓
✓
✓
✓
✓
✓
✓

IV.
✓

Relationchild-father
child-father
child-father
child-motherwife-husband
child-father
child-motherchild-father

Judge
✓
✓
✓
✓
✓
× not extracted
× not extracted
× not extracted

E XPERIMENT

We successfully built character networks from fourJapanese comics. Figure 3 shows the output from the Naruto
dataset. In this section, we focus on the evaluation of ouralgorithms for creating semantic relations.

✓✓

A. Method
We built four datasets from four comics: Naruto, Vols. 4650, Dragon Ball (kanzenban) Vols. 29-34, Boys Over Flowers,
Vols. 16-20, and Space Brothers, Vols. 1-5. The average
number of characters (four characters in Figure 2) was 10,985.
6,581 characters (one character in Figure 2) out of 10,985 hadwords in balloons (60.1%).

&KLOG)DWKHU

1DUXWR
8]XPDNL

Character 2
Choza Akimichi
Sakumo Hatake
Minato Namikaze
Tsume Inuzuka
Fukasaku
Nagato’s father
Nagato’s motherInoichi Yamanaka

Recall: 5/8 = 0.625 (without and with inference)

0LQDWR
1DPLND]H)XNDVD
NX

Character 1
Choji Akimichi
Kakashi Hatake
Naruto Uzumaki
Kiba Inuzuka
Shima
NagatoNagatoIno Yamanaka

C ORRECT RELATIONS AND RECALL

We evaluated our algorithms with and without inferences.

.DNDVKL+DWDNHThe following are the evaluation measures for extracting
relations:
6DNXUD+DUXQR

1DJDWR.RQDQ<DKLNR-LUDL\D6DL

P recision =

6KLND
PDUX
1DUD

Recall =correct extracted relations
extracted relations

correct extracted relations
correct relations to be extracted

F − measure =Fig. 3.
Generated overview character networks for Naruto Vols. 46-50(without inference).

character), Nagato, and Sakura Haruno are displayed as thethree main characters. Since Kakashi Hatake is selected as
being among the top three with Naruto Uzumaki and Sakura
Haruno, he is displayed between them. The child-father relation name is displayed alongside the line between Naruto
Uzumaki and Minato Namikaze. Four infrequent characters,
as categorized in group 4 (Yahiko, Jiraiya, Minato Namikaze,and Shikamaru Nara), are displayed in the character networkfrom their relatedness to the main characters.

2 × P recision × Recall.
P recision + Recall

For our Naruto example, Table III shows how to calculate
Precision and Table VII shows how to calculate Recall.
We generated six kinship relations, five of which werecorrect without inference, so the precision without inferenceis 0.833. Seven (one more) relations were generated withinference, five of which were correct, so the precision withinference is 0.714 (Table III). Eight kinship relations exist in
the dataset, and five were extracted, so the recall is 0.625 (Table
VII).
B. Results and Analysis

Table IV lists the ranking of character appearances, andTable VI ranks the relatedness between characters. Pleasecheck these tables for the characters and relations (marked
as “Display” in the final column) that appear in Figure 3.

In all four comics, we identified the following relations and their amounts: 19 child-father, 18 child-mother, 11
wife-husband, 3 person-older brother, 2 person-older sister,
2 grandchild-grandfather, 1 grandchild-grandmother, and 1
person-uncle. We identified no person-aunt relations.

Note that we chose without inference for automatic display
networks based on our experiment.

Table VIII shows the experiment results without/with inference. Overall, without inference is better in precision andf-measure and with inference is better in recall.

TABLE VIII.

Comic
NarutoDragon Ball
Boys Over Flowers
Space Brothers
Average

Without inference
PreciRecall
Fsion
measure
0.833
0.625
0.714
0.666
0.166
0.265
0.368
0.583
0.451
0.411
0.411
0.411
0.569
0.446
0.460

TABLE IX.

Relationchild-father
child-motherwife-husband

E XPERIMENT RESULTS
With inference
PreciRecall
Fsion
measure
0.714
0.625
0.666
0.714
0.208
0.322
0.303
0.833
0.444
0.269
0.411
0.325
0.500
0.519
0.439

R ESULTS OF T HREE R ELATIONS

Without Inference
PreciRecall
Fsion
measure
0.688
0.500
0.579
0.714
0.333
0.454
0.500
0.364
0.421

With Inference
PreciRecall
Fsion
measure
0.500
0.591
0.542
0.412
0.389
0.400
0.500
0.364
0.421

Table IX shows the results for three relations. For the childfather and child-mother relations, without inference is better inprecision and f-measure and with inference is better in recall.
For wife-husband relations, nothing was added in the inference
process, and the result was identical.
We explain the following three types of errors using ourNaruto example: precision, recall, and inference.
For the precision error, Fukasaku and Naruto Uzumaki appear together many times. Since Fukasaku calls his wife Shima“mother,” Naruto Uzumaki-Fukasaku (C-M) was extracted.
For the recall error, we failed to extract Nagato-Nagato’s
father (C-F), Nagato-Nagato’s mother (C-M), or YamanakaInoichi Yamanaka (C-F). There was only one “father call” andone “mother call” from Nagato to other characters. In thesecases, Nagato’s father and mother were in the same frame.
Our algorithms could not identify which person was the father
or the mother. For the Yamanaka case, there was no “father
call” in the dataset.
From the Naruto Uzumaki-Minato Namikaze child-father
relation and the Fukasaku-Naruto Uzumaki child-mother relation, a Fukasaku-Minato Namikaze grandchild-grandfather
relation was created. Basically, inference error occurs fromincorrect relations. One apparent solution is that the user
should check and correct his/her answers before the inference
process and do based on correct relations.
From the precision and recall failure analysis, we identified
room for improvement and the following possible solutions:(1) add terms to the dictionary or modify the call extraction
heuristics to properly capture the calls; (2) modify how to score
the relations; (3) extract relations without using calls.
V.

D ISCUSSION

Finally, we discuss the contributions of our research.
First, to the best of our knowledge, our work (including
our previous work) is the only research that creates character
networks from comics. We modified our previous algorithm
for creating character networks and added kinship relations tothem.
Second, we implemented our algorithms and successfully
built overview character networks from four popular Japanesecomics: Naruto, Dragon Ball (using different volumes fromour previous work), Boys Over Flowers, and Space Brothers.
Third, we evaluated our algorithm for extracting kinshiprelations and found that without inference is better in precisionand f-measure and with inference is better in recall. Without
inference is suitable for automatic generation, and inference
should be used after correcting the initial relations.
Even though basic algorithms using frames are applicable
to any language, some heuristics of extracting semantic relations depend on culture and language. For example, for wifehusband relation extraction, we used Japanese married couples
with children who refer to each other as “father” and “mother.”
We must consider heuristics for other cultures and languages.
Future work will improve the extraction of relation algorithms. We need to evaluate how to generate overview
networks algorithms and consider other relations, such as“friends” or “rivals,” to make character networks more useful.
Other types of character networks such as “group” should also
be developed in the future.
VI.

C ONCLUSIONSWe presented a method to create character networks fromcomics using the frames and the words in balloons to identify
and understand comics. First, we built a dataset that contains
a comic’s characters and their words in balloons separated byframes. Second, we extracted the semantic relations among
characters using the words in balloons and frames. Third,
we developed character networks by identifying critical characters by their relatedness and added semantic relations to
the networks. We implemented our approach and successfully
built character networks from four popular Japanese comics.
We also evaluated kinship relation extraction algorithms using
these comics.
R EFERENCES[1] H. Murakami, R. Kyogoku, and H. Ueda, “Creating character connections
from manga,” in Proceedings of the 3rd International Conference on
Agents and Artificial Intelligence (ICAART 2011), vol. 1, 2011, pp. 677–
680.
[2] D. K. Elson, N. Dames, and K. R. McKeown, “Extracting social networks
from literary fiction,” in Proceedings of the 48th Annual Meeting of the
Association for Computational Linguistics (ACL 2010), 2010, pp. 138–
147.
[3] J. Lee and C. Y. Yeung, “Extracting networks of people and placesfrom literary texts,” in Proceedings of the 26th Pacific Asia Conferenceon Language, Information, and Computation (PACLIC 2012), 2012, pp.
209–218.
[4] H. Nishihara, “Extraction of relations among characters in literary texts,”
Master’s thesis, JAIST, 2015.
[5] D. Kokkinakis and M. Malm, “Character profiling in 19th century
fiction,” in Proceedings of Language Technologies for Digital Humanities
and Cultural Heritage Workshop, 2011, pp. 70–77.
[6] J. Goto, Y. Yagi, A. Aizawa, and S. Sekine, “Generation of correlation
charts from tv programs based on anaphora resolution,” in The 22nd
Annual Conference of the Japanese Society for Artificial Intelligence,
2008.
[7] T. Ogasawara, T. Takahas, I. Ide, and H. Murase, “Construction of ahuman correlation graph from broadcasted video,” in The 22nd Annual
Conference of the Japanese Society for Artificial Intelligence, 2005.
[8] Y. Matsuo, M. Mori, J.and Hamasaki, K. Ishida, T. Nishimura, H. Takeda,
K. Hashida, and M. Ishiduka, “Polyphonet: An advanced social network
extraction system,” in Proceedings WWW2006, 2006, pp. 397–406.