言語処理学会 第30回年次大会 発表論文集（2024年3月）

Genre-based Character Network Analysis and Emotion
Sequence Analysis for Manga
Megha Sharma1 Yoshimasa Tsuruoka1
1 The University of Tokyo{meghas,yoshimasa-tsuruoka}@g.ecc.u-tokyo.ac.jp

Abstract
This paper explores the relationship between emotion
sequences and character network graphs from Japanesecomics to explore trends among diﬀerent genres. We propose a framework to extract emotion sequences from themanga, and analyse whether signiﬁcant correlations exist
between temporal pages and emotions of the manga as wellas between emotion variables. We also detect the peaks andvalleys to ﬁnd correlations with genres. We build social
network graphs from the characters in pages to analyse thestructural relationship across genres and extract featuresfrom the graphs and report on the trends across genres.
Our results suggest that some preliminary trends exist in
the current scope of the study.

1Introduction

Japanese comics, manga, are a multi-media art form
where visual cues (drawings) and textual cues (dialogues)
are used to create a complete story [1]. Additional visualelements such as style and onomatopoeia are used to enhance the reading experience. Manga analysis allows us tounderstand about the content and organisation of the manga
through a social and reﬂective lens. Manga analysis not
only beneﬁts a social interpretation of multi-media, but it
also supports applications such as manga translation [2],in-painting [3], and sketch-to-manga conversion [4].
Existing studies on manga analysis show that societal
inﬂuence plays an important role in the development ofmanga. Research on gender-based and genre-based division in dialogues reveal mangas are inﬂuenced by dialogue
patterns that mimic societal usage over the years [5]. Infact, Sugishita and Masuda [6] argue that manga story-lines
exhibit character networks that reﬂect empirical social networks in society.

In this paper, we aim to analyse manga characters andcontent through a lens of genre. Our contributions aretwo-fold. We view our work as an extension of Sugishita
and Masuda’s [6] work by extending the character network
analysis to recognise genre-based patterns. Moreover, wealso conduct a brief emotion sequence analysis based ongenres. Our results ﬁnd preliminary trends in the sequences
and character graphs.

2Method

We short-listed 46 books from the Manga109 dataset[7] [8], which is annotated with face, dialogue and bodybounding boxes. There are 12 genres deﬁned in the dataset.
We use four books from each genre except for horror where
only two books are available. For character network analysis we only use two books from Four-Frame Cartoon (FFC)
genre due to available data and annotation conﬂicts. We
approach the analysis using two methods: Emotion sequence analysis and character network analysis. The former utilises the continuous sequence of emotions detectedusing the character faces and dialogues. The latter builds
character networks of the characters appearing in manga
and analyses the resulting graphs. In this section, the details of both methods are explained.

2.1

Emotion Sequence Analysis

We deﬁne emotions using Russell’s four quadrant theory that maps emotions as a function of arousal (intensity)and valence (positivity) [9]. Emotion is detected using thedialogues (text) of the manga page as well as the character
faces. To do so, a face emotion detection model and atext emotion detection model are trained. The face emotion detection model is trained on a dataset of 509 manga
character faces associated with 11 emotion classes1）. An1） https://www.kaggle.com/datasets/davidgamalielarcos/mangafaces-dataset― 1963 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

deﬁned for emotions above or below a threshold of fourand two respectively when normalised on a scale of one
to ﬁve. The statistics of the results of all features can befound in appendix A.1. These results are made only using
the statistically signiﬁcant coeﬃcients to avoid noise frominsigniﬁcant correlations.

2.2

Character Network Analysis

From the analysis by Sugishita and Masuda [6], we takea social network approach to our manga analysis by genre.
Hence, we create temporal bipartite graphs where one layerof nodes are the pages and the other are the characters. AnFigure 1

edge between the character and the page exists if the char-EﬃcientNet model [10] with a custom classiﬁcation layer

acter exists on the page. Using this graph, we can project it
on a space of only the character nodes to capture the charac-

Emotion sequence for manga titled “Paraiso Road” by
Kanno Hiroshi. The emotions are normalised from 1 to 5

achieves a validation accuracy of 77.7%. We convert theemotion categories to valence and arousal values based on

ter network. Examples of extracted character networks arethe NRC-VAD lexicon[11]. We use a pre-trained Japanese-graphs can vary from size and structure. Complete graphs

BERT2） model for text emotion detection.

We ﬁne-tune

show equal distribution of character interactions throughthe model on a dataset of 30,000 social media blog posts

the manga, while star-like graph show a protagonist-driven[12]. The model achieves an accuracy of 61% on sentimentstory. On the other hand, subgroups may exist as seen in(valence) detection. A case study of emotion sequences isﬁg. 4 despite a well-connected “protagonist” (node withshown in ﬁg. 1, where the normalised emotion variables ofhighest degree). These might suggest such stories havearousal and valence are plotted. The plots suggest spikes

sub-plots that feature diﬀerent characters. Our goal is toin arousal at three peaks, with a considerable pace change

analyse these properties to detect any trends between thetowards the end of the book at page 100. The sharp change

genre of these novels.

shown in ﬁg. 2 - ﬁg. 4. As seen in the ﬁgures, the characterin arousal and valence boosts the emotion to a high point,

Similar to emotion sequence analysis, we extract features

which can signal a happy end to the story line. This isfrom the graphs to study for the discussion. We extract thenumber of nodes and edges as N and M respectively. We

reﬂected as the story reaches a positive pay-oﬀ in the ﬁnal few pages. Qualitative analysis of emotion sequences
can help understand the emotional arc of the story-line andalso measure the average degree of the graph as K and theidentify climactic points (points of sharp peaks or valleys).

measures the variation trends in the degree of all nodes incoeﬃcient of variation of the degree as KCV. The latter

We extract features from the emotion sequences that will

the graph by taking the ratio of the standard deviation to

be used to understand behaviour by genre. These features

the mean. We also measure the degree of the “protago-

can be categorised into three: detecting (1) temporal rela-nist”, KP. KP is also normalised by dividing by the rest

tions, (2) manga feature relations, and (3) peaks and val-of the number of nodes. Degree assortativity, R, measuresleys. For temporal relations, we calculate the Kendall tau

whether nodes with similar degrees are closer to each othercorrelation coeﬃcient between temporal (pages) and otheror further apart [13]. Clustering coeﬃcient, C, measurescontinuous manga features such as arousal, valence andthe extent of clustering as a triangle in the graph [14].

text length. We also compute Pearson correlation among

3Discussion

3.1

Emotion Sequence Analysis

the three manga features. Finally, we keep track of number of peaks and valleys detected for each book in botharousal and valence sequences. The peaks and valleys are

We have plotted the average temporal relation coeﬃ2） huggingface.co/minutillamolinara/bert-japanese ﬁnetunedsentiment-analysis

cients in ﬁg. 5 for each of the 12 genres. Science ﬁction― 1964 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

Figure 2

A completely connected social
graph from OL Lunch by Sanri Youko.

Figure 3

A star-like social graph fromTouyou Kidan by Tojo Miho.

Figure 4

A social graph with sub-groups
from Tsubasa No Kioku by Sato Harumi.

had no statistically signiﬁcant results in this step. Apartfrom horror, all genres exhibit mostly a negative correlation
with time. This might suggest that the manga has decreasingly lower arousal and valence with the turn of the pages.
However, the relationship is not very strong, ranging from0 to -0.17 at best. Animal genre has the most negative
correlation between the dialogue text length and the page
number, suggesting that the dialogues get shorter with thestory. On the other hand, Suspense and Historical dramaboth show a positive correlation between text length andpage number. Historical drama in particular is the longest
manga on average, with a text character length of over 28K.

Figure 5

Temporal correlations of emotion sequences by genre.
Empty bars suggest no statistically signiﬁcant results were observed.

Horror has the most positive correlation with valence andarousal. However, it should be noted that these signiﬁcantvalues are taken from only one book in the genre as theFinally, we look into the content of the valence andother has no signiﬁcant relation. This highlights a poten-

arousal values across pages using peak and valley detec-

tial limitation of the study, the limited books used heretion. In general, the percentage diﬀerence between peakact as a barrier to the understand the performance acrossand valleys is over 97%, implying valleys are far moregenres.
In particular, we found a very strong correlation between

common than peaks across genres. In particular, suspense
genre has the lowest number of arousal valleys, suggesting

arousal and valence across all genres. The lowest corre-that arousal is not usually on the lower end, while Animal

lation is that of 0.7 (Animal genre), achieving up to 0.9(FFC genre). This strong correlation seems to point thehas the highest number of arousal valleys, suggesting thatdata mostly stays in the ﬁrst and third quadrant of Russell’sRomance also have the highest number of valence valleys,

four quadrants. We believe this narrow view of emotion issuggesting sentimental lows in the story.

arousal is usually on the lower end. Humour and Love

reﬂective of the capacity of the emotion detection models

In general, we found that arousal, valence, and text length

rather than the actual manga emotion content. All genres

do not hold signiﬁcant diﬀerences among genre. One rea-also have a positive correlation between emotion variables

son for such results could be the limited data and model(arousal, valence) and text length. This relationship is theperformance. Moreover, manga is more generally diﬀer-

lowest for Love Romance and Romantic Comedy and high-entiated by intended audience such as Shoujo (manga forest for FFC, implying that romantic genres tend to associategirls) and Shounen (manga for boys) are more common

little arousal growth with text length of the manga. Thegenre descriptions for Japanese comics [5]. However, we

relationship between valence and text length remains stableleave the analysis as per gendered genres as future work
as it requires simultaneous studies of gender and genre

between 0.6 to 0.8 across all genres.

diﬀerentiation.

― 1965 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

3.2

Character Network Analysis

The statistical results from character network graphs isavailable in table 1. On average, there are 28 charactersdeﬁned for a manga. However, the variety of network
size varies among genres. Our results ﬁnd that Historical Drama has the largest number of characters and edges
on average, which falls in-line with the text length of thebooks. Four-Frame Cartoons (FFC) tend to have the smallest number of characters and edges. An example graph ﬁg.
2 shows how FFC genre can be diﬀerent from other gen-Figure 6 R, C values for each genre based on the character
network graphs. Manga exhibits disassortative networks

res seen in ﬁg.4 and 3. This is also conﬁrmed by the low
KCV we ﬁnd for FFC, which indicates that the degree ismore homogeneous than diverse, implying a small numberof characters interact with similar number of characters

hand, all genres exhibit a high level of clustering. Theseclustering coeﬃcients are largely similar, with an averageof 0.83 across all books. This implies the graphs exhibit alarge number of triangles.

throughout the manga.
The coeﬃcient of variation of the degree of graph lies

In general, character network analysis reveals some inter-between 0.00 (completely connected graph) to 1.15 (highesting trends across genres, which can be analysed further

variation in the graph). On average, KCV takes a value

with more books and longer volumes.

of 0.62, with a majority of manga falling under 1. This

Table 1 Statistical results for all genres for character network
analysis. N is for number of nodes, M is for number of edges, Kis for degree of the graph, KCV is the coeﬃcient of variation forthe degree, KP, is the degree of the “protagonist” of the story, R
is the degree assortavity and C is the clustering coeﬃcientis consistent with the results by Sugishita and Masuda [6]where smaller social networks, as seen in manga, exhibit
similar characteristics. Historical drama achieves the highest graph degree on average, while animal genre has thelowest. These results are intuitively in-line with resultsfrom N and M, with a positive correlation with both. TheStatistics
meanstdminmaxN
28.45
18.95
7.00
85.00

M109.98
90.85
13.00
402.00

K
7.24
2.29
2.80
13.13

KCV
0.62
0.26
0.00
1.15

KP
0.84
0.20
0.26
1.00

R
-0.28
0.20
-0.67
0.34

C
0.83
0.07
0.67
1.00

normalised degree of the protagonist on average is 0.84,
which shows high level of connectivity in manga social
networks. This could suggest, most manga are protago-

4nist driven, a similar conclusion derived in [6]. Romantic
Comedy has the signiﬁcantly lowest KP among the genres

This paper studies the content of manga in diﬀerent genres through two lenses: emotional and social. Our results

at 0.6, while other genres are all above 0.75. On structural

discuss some interesting trends revealed in the emotion se-analysis of the character graphs, it is evident that Romantic

quence analysis and the character network analysis. How-

Comedy is more likely to exhibit graphs with sub-groups

ever, these results are based on a limited set of data andof characters similar to ﬁg. 4. Whether these exist due

requires further investigation to achieve a more substantial

to sub-plots can not be veriﬁed from graph structure andstudy of manga analysis. Hence, we propose the follow-emotion sequence alone. Detailed results are available ining future directions. An important bottleneck for emotion

appendix A.2.

sequence analysis are the performances of the emotion

Future Work and Conclusion

As seen in ﬁg. 6, character networks are all dissassor-recognition models, hence, we plan to experiment withtative, which means that nodes with dissimilar degrees are

diﬀerent modeling architectures to improve emotion recog-closer to each other. This interesting result is also exhibited

nition in text and visual cues. We also propose deeper se-in [6]. We ﬁnd that horror and Four-frame cartoons exhibit

quence analysis, especially inspired from bio-informatics.

the lowest dissassortativeness. This is due to the particularwell-connected graphs seen in both genres, where the story

Solomon et al. [15] used such sequence analysis techniques

is not as protagonist-centered as other genres. On the otherto detect motifs in conversations. To conclude, our results
invite a further analysis of manga through various lenses.

― 1966 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

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A

Appendix

A.1

Statistic Results from Emotion Sequence Analysis

Table 2 Statistical results across all genres. page arousal, page valence, page text, arousal text, arousal valence, valence text are
correlation coeﬃcients between both variables. valence peaks, valence valleys, arousal peaks, arousal valleys are number of peaks
and valleys for valence and arousal respectively.
Statistic

page arousal page valence page text arousal text arousal valence valence text valence peaks valence valleys arousal peaks arousal valleys

mean

-0.04

-0.05

-0.06

0.41

0.74

0.74

5.04

15.70

5.52

14.04

std

0.11

0.11

0.13

0.17

0.11

0.09

3.84

7.23

3.69

6.68

min-0.44

-0.41

-0.43

0.00

0.54

0.56

1.00

1.00

1.00

4.00

max

0.22

0.17

0.28

0.85

0.97

0.92

14.00

33.00

18.00

35.00

A.2

Genre-based Comparison of Character Network Graph Features

Figure 7

Figure 8

Number of nodes and edges divided by genre.

Degree (K), Coeﬃcient of variation of degree (KCV) and protagonist normalised degree (KP) divided by genre.

― 1968 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).