EMOTIONAL PERCEPTION OF DEATH IN ANIMATED FILMS - SENTIMENT ANALYSIS OF COCO AND SOUL'S SCRIPTS AND REVIEWS - DIVA PORTAL

Emotional Perception of Death in Animated
 Films
 Sentiment Analysis of Coco and Soul’s Scripts and Reviews

 Li-Hsin Hsu

Department of ALM
Theses within Digital Humanities
Master’s thesis (two years), 30 credits, 2021, no.4

Author
Li-Hsin Hsu

Title
Emotional Perception of Death in Animated Films: Sentiment Analysis of Coco and Soul’s Scripts and Reviews

Supervisor
Nadzeya Charapan

Abstract
This thesis aims to understand the emotions expressed by adults watching animated films with death topics through
sentiment analysis. The research is a quantitative sentiment analysis from the perspective of distant reading.
 The previous studies on death scenes in animated films have only focused on child audiences. However, the
age group of the audience of animated films is extensive; thus, it is necessary to analyse the sentiments of adult
audiences. This thesis attempts to collect two movies produced by Pixar studio: Coco (2017) and Soul (2020), as
well as their audience reviews on IMDb, a total of 600, for cross-comparison. Additionally, it analyses the content
containing death in the reviews to understand better adult audiences’ emotional expressions on the subject of death.
 The analysing results show that the positive sentiment scores of the comments containing death are slightly
lower than the scores of all the reviews, and the scores of the negative sentiments do not differ much. However,
positive emotions still dominate these comments that contain death. The emotional performance between the script
and the reviews is roughly similar. Still, the emotional intensity of the comments is higher than that of the script,
indicating that the audience is willing to show their emotions on the public online film platform. Future research
is recommended to conduct analysis together with other NLP analysis methods or close reading to explore more
details of the content.

Key words
Pixar, IMDb, Distant Reading, Sentiment Analysis, Emotions, Death

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Table of contents

Introduction ......................................................................................... 7
 Background .......................................................................................................... 7
 Research Purpose and Questions ......................................................................... 9
 Delimitations ...................................................................................................... 10
 Thesis Outline .................................................................................................... 10
 Previous Research .............................................................................................. 11
 Pixar’s Animated Films ............................................................................................ 11
 Death in Animated Films .......................................................................................... 12
 Theoretical Framework ...................................................................................... 16
 Emotion Theory ................................................................................................. 16
 Methodology ...................................................................................................... 17
 Analysis Method: Sentiment Analysis ...................................................................... 18
 Data Analysis: Distant Reading ................................................................................ 21
 Analysis Tool ............................................................................................................ 22
 Data Collection ......................................................................................................... 24
 Method of Data Analysis .......................................................................................... 26
 Ethical Considerations .............................................................................................. 27
Analysis and Findings ....................................................................... 29
 Introduction of Context ...................................................................................... 29
 Profile of Pixar Animation Studios ........................................................................... 29
 Coco (2017) .............................................................................................................. 29
 Soul(2020)................................................................................................................. 30
 Profile of IMDb ........................................................................................................ 30
 Pre-processing Findings ..................................................................................... 31
 Word Frequency ................................................................................................. 33
 Scored Sentiment ............................................................................................... 34
 Findings of Sentiment Analysis ......................................................................... 38
 Findings of Coco’s Script ......................................................................................... 38
 Findings of Soul’s Script........................................................................................... 41
 Findings of Coco’s IMDb Reviews .......................................................................... 44
 Findings of Soul’s IMDb Reviews............................................................................ 45
 Findings of Review with Death ................................................................................ 46
 Findings of Comprehensive Analysis ....................................................................... 47
Discussion and Conclusion ............................................................... 51
 Discussion of Research Questions ..................................................................... 51
 Percentage of Deaths in Reviews.............................................................................. 51
 Sentiment Analysis of Reviews with Death.............................................................. 52
 Sentiment Analysis Differences between Reviews with Death and all Reviews ...... 52
 Comparison of Sentiment Analysis Results between Scripts and Reviews .............. 53
 Application of Sentiment Analysis ........................................................................... 54
 Conclusion ......................................................................................................... 55
 Limitations ................................................................................................................ 56
 Suggestion for Future Research ................................................................................ 56

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Bibliography ...................................................................................... 59

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Table of figures

Figure 1. Plutchik’s wheel of emotions …………………..…………………........17
Figure 2. Sentiment Analysis methods…………………………………..…..........19
Figure 3. First ten words in NRC word-emotion association lexicon …………….24
Figure 4. Users’ age distribution of Coco ………………………………….….....25
Figure 5. Users’ age distribution of Soul …………………………………………26
Figure 6. The top 20 most frequently appearing words in Coco’s reviews …….…33
Figure 7. The top 20 most frequently appearing words in Soul’s reviews ………...34
Figure 8. Coco’s script sentiment distribution …………………………………....38
Figure 9. Coco’s script sentiment distribution in 8 basic emotions ……….……..39
Figure 10. sentiment changes in the Coco script …………………………………40
Figure 11. Soul script sentiment distribution …………………………….……….41
Figure 12. Soul script sentiment distribution in 8 basic emotions …….………….42
Figure 13. sentiment changes in the Soul script ………………….……………… 43
Figure 14. Coco’s reviews sentiment distribution ………………….…………....44
Figure 15. Coco’s review sentiment distribution in 8 basic emotions …………...44
Figure 16. Soul’s reviews sentiment distribution ………………………………...45
Figure 17. Soul’s review sentiment distribution in 8 basic emotions ………….…45
Figure 18. Coco’s reviews with death vs. all Coco’s reviews ………………….…46
Figure 19. Soul’s reviews with death vs all Soul’s reviews ………..……………...47
Figure 20. Compare Coco script with Coco’s reviews ……………..…………….48
Figure 21. Compare Soul script with Soul’s reviews ………………..……………49
Figure 22. All text in emotion wheel ……………………………………………..50

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Table of tables

Table 1. No lemmatisation, compared with the Coco script after lemmatised .…..32
Table 2. No lemmatisation, compared with the Soul script after lemmatised …….32
Table 3. No lemmatisation, compared with the Coco’s review after lemmatised....32
Table 4. No lemmatisation, compared with the Soul’s review after lemmatised …33
Table 5. Sentiment analysis of Coco’s script ………………………….…………34
Table 6. Sentiment analysis of Soul’s script ……………………………………..35
Table 7. Sentiment analysis of Coco’s review …………………………………...35
Table 8. Sentiment analysis of Soul’s review …………………………………….36
Table 9. Coco’s script emotions scores ………………………………..…………36
Table 10. Soul’s script emotions scores ………………………………………….36
Table 11. Coco’s reviews emotions scores ………………………………………37
Table 12. Soul’s review emotions scores …………………………………………37
Table 13. The emotional scores of the death-related vocabulary …………………46

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Introduction

Background
Death is an issue that everyone needs to face. According to the existing research,
people exclude the topic of death from social life, feel uncomfortable about
discussing death, and even regard it as a taboo (Gire 2014, p. 3; Walter 1991, p.
293; Corr 2016; Gellie, Mills, Levinson, Stephenson, and Flynn 2015 cited in
Miller-Lewis et al. 2020, p. 243).
 Movies are a suitable medium for talking about life and death. Films can serve
as an anxiety buffer against the fear of death (Rieger et al. 2021, p. 367). The
interactions between the characters can stimulate audiences to reflect on their lives
and promote aesthetic experiences (Oliver and Hartmann 2010, p. 130; Bartsch and
Hartmann 2017, p. 30). The film’s emotional expression and narration can also
satisfy the need of the audience’s emotion releasing (Janicke and Oliver 2017, p.
278) and encourage people to share thoughts with others (Arriaga et al. 2019, p. 7).
 Among several movie genres, this thesis attempts to focus on the perspective
of animated films as they have always been regarded as a particular category. The
graphs in animation films are conceptualised, producing behaviours and actions to
develops into a complete story (Barak, Ashkar and Dori 2010, p. 840). This feature
also has made many scholars dive into this field for an extended period. In 2005,
Cox, Garrett and Graham proposed research on how children understand the
concept of death in Disney animation films. Many scholars from different
professional fields, such as education and psychology, continue to follow up. From
these studies over the years, they have pointed out that animated films are an
excellent tool to open the challenging topic of death effectively and can guide
children to express emotions. It is an unfortunate fact that these subjects only
focused on discussing children. However, animated films are not only attractive to
children; many adult audiences also love them. Even though adults realised the
meaning of end-of-life, they still need to continue to learn how to release and adjust
their anxiety about death and understand the importance of life through death
further.
 Online film review forums is a suitable platform for collecting feedback from
adults after watching movies. Nowadays, more and more people share their reviews,
experiences and ratings of movies online with the trend of sharing information with
others by publishing texts on the platform, further strengthening interpersonal
relationships and social interaction (Tefertiller, Maxwell and Morris 2019, p. 4). Film
review platforms gradually become a new kind of social media (Fatemi and
Tokarchuk 2012, p. 2), and the large amounts of feedback on the site become the data
of analytical value. With the vigorous development of social media, sentiment

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analysis has gradually attracted everyone’s attention and became critical. Sentiment
analysis, a natural language processing method, is one of the most widely used
analysis methods, allowing analysts to identify meaningful insights from raw data
(Kharde and Sonawane 2016, p. 5). Because it is the subjective feeling expressed in
the study text, it can be analysed to understand the opinions, attitudes, emotions, and
perceived needs of the opinion publishers profundly (Liu 2012, p. 20). Online film
forums provide audiences with short texts to express their views on movies. By
collecting and analysing these texts, analysts can investigate the audience’s
experience and satisfaction more systematically. In short, sentiment analysis in
movie reviews makes the task of summarising opinions easier by extracting the views
expressed by the reviewers (Hu and Liu 2004, p. 170), which also represents the
relationship between the audience and their interaction experience with movies
(Mokryn et al. 2020, p. 477).
 Given the gaps in adults’ emotional perception of animated films in the existing
research, this thesis attempts to make up a piece of the puzzle by trying to use
sentiment analysis to do text mining and probes to understand adults’ emotions and
cognitions after watching death scenes in animated movies. The concept is based
on distance reading, using automated procedures for text exploration to processing
a large amount of text. Through model calculation, the valuable information
elements are chosen from the text so that analysts can grasp the complete picture of
the text faster (Drucker 2017, p. 630). Coco (2017) and Soul (2020), produced by
Pixar Animation Studios, were selected for the analysed material. Pixar is a leader
in the current animation film industry and constantly launches movies with
innovative topics (Catmull 2014, p. 7). The theme of death is one of them, and death
is crucial in these two selected films. In these two stories, the audience can see how
the protagonists face death and deal with their negative emotions. Furthermore, the
audience can see the depiction of the afterlife and experience the ground-breaking
worldview. On the side of the movie reviews selection, this thesis collects the users’
reviews of these two movies on IMDb, which is currently the world’s most famous
movie review platform; collected 300 top-voted reviews which have the most votes
on each film, analysing these comments and understanding adults’ reactions and
comprehension to death.
 This thesis aims to tackle the research problem of the lacking research on how
the adult audience perceives death in animated films. Because when referring to
animations, people often classify them as “children’s film” or equated with it
(Zornado 2008, p. 2). However, “as had all animations at the time, Mickey started
life as a cartoon targeted at adults (Madej and Lee 2012, p. 70)”, and the content of
animated movies is able to capture the hearts of multiple generations. An animated
film is a unique form of artistic expression; it reflects “the different strands of
activity and thinking about animation as a process, an art, a craft, a representational
idiom, and a site addressing ideas and issues, most specifically memory and

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emotion (Wells 2012)”. Although adults are the initial target audience (Madej and
Lee 2012, p. 70), they are often ignored in the study of animated films. The topics
discussed in animated films are broad and contain many difficult subjects－death
is one of them. The number of death scenes in animated movies is far greater than
our imagination, but death is not a popular topic of discussion in actual society.
Many researchers have conducted in-depth studies on how children understand the
concept of death from animated movies; however, they often ignore that adults are
also one of the audiences of animated films. Although adults can realise that death
is an unrecoverable state, it does not mean that adults can feel calm about this issue.
 Many people express their rich emotions after watching movies on online
movie platforms. They are expressing their opinions and sharing ideas with other
audiences. As a new type of social media, online film review sites partially replace
traditional social interaction forms (Tefertiller, Maxwell and Morris 2019, p. 10).
People’s opinions on online platforms have become content that cannot be ignored
and worthy of analysis. Therefore, the thesis focuses on using sentiment analysis to
deeply study how adults feel about death and the emotions they generate after
watching Coco and Soul produced by Pixar. The reason for choosing these two
films is that they describe the characters facing death and portray the afterlife so
that death can be the core of these two films.
 Sentiment analysis, also known as opinion mining, is Natural Language
Programming (NLP). Through sentiment analysis, analysts can present abstract
language with more concrete emotions. Hence, it is possible to explore, evaluate
and analyse the sentiments and attitudes hidden in a large number of texts. The
technique is currently one of the mainstream methods for analysing the opinions of
the masses (Liu 2012, p. 2). This thesis analyses scripts and 600 user reviews of
Coco and Soul on IMDb (www.imdb.com) together.

Research Purpose and Questions
This thesis analyses adults’ emotional perception of death after watching animated
films through sentiment analysis of the scripts and reviews. On the one hand, Willis
(2002) pointed out that children and adults have different views on four dimensions:
reversibility, finality, inevitability, and causality, but this field has a gap in the
perspective of adults as research objects (Tenzek and Nickels 2017, p. 62).
Therefore, despite the vast age distribution of the audience of animated films, it is
hard to know the impact of animated films on adults. On the other hand, in the past,
the research required several researchers to watch all selected movies before scoring
(Graham, Yuhas and Roman 2018, p. 12), so that if researchers would like to
observe this phenomenon for a long time, it will be time-consuming.
 Therefore, the thesis conducts sentiment analysis to operate text analysing.
Based on this goal, the main research topic of this thesis is:

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RQ1. What is the adults’ emotional perception of death after watching Coco
 (2017) and Soul (2020) through the sentiment analysis of reviews?

 RQ2. How do the findings from the sentiment analysis of the scripts correspond
 with the results of viewers’ review analysis?

 This thesis’s analysis focuses on these two issues, using sentiment analysis, a
technique of text mining, to find out what they perceive and what they talk about
the death topic that many people are unwilling to discuss in public.

Delimitations
According to the choice of analysis data, it is necessary to draw a limit here. This
thesis’s analysis is an in-depth exploration and analysis of the two films of Pixar,
Coco and Soul, rather than a comprehensive study of animated films. Therefore, the
final data cannot be regarded as a universal analysis result of animated films.
 The content discussed in this thesis is only a few of the parts produced by Pixar.
As the previous concerns raised by other scholars, works from different studios
might cause different results, because every studio has disparate storytelling styles,
story themes and values. Additionally, the film review website of this thesis is only
collected by IMDb. Therefore, forums of different regions and different languages
may have different analysis results due to varying sources of insights.

Thesis Outline
This thesis is composed of three parts: the introduction, the analysis and discussion
and conclusion. In the introductory chapter, the research aim, purpose and method
of the thesis are explained; it provides an overview of the research field,
highlighting the previous research and the contributions related to the study. Further,
it describes the viewpoints and theories adopted in this thesis. In the last part of this
section, empirical research methods will be explained, including material selection
and collection, analysis process, and ethical issues when conducting research.
 The second chapter is data analysis, which will describe the analysis process in
detail. First, this part introduces the context and explains how to preprocess the text.
Afterwards, the texts are sequentially analysed by word frequency and sentiment
analysis and cross-compared these analysis data.

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The final part of this thesis is the discussion and conclusion. This section uses
the analysis results to answer the research questions firstly, and finally describes
some of the limitations of this thesis and provides some opinions on the future
research of the research topic of death in animated films. There is a conclusion of
the entire thesis at the end.

Previous Research
This chapter intends to emphasise the existing gaps in the literature. There are three
sections addressing research on Pixar’s animated films, death in animated films,
and sentiment analysis.

Pixar’s Animated Films
As a high-profile research target, animated films are closely related to their roles as
members of the cultural industry. With its vigorous development, animation movies
have gradually been marginalised in film and media studies and have become a new
research field of their own (Herhuth 2015, p. 1); it is an artistic expression
combining graphics technologies and storytelling. Through graphics technologies,
an animated world that is entirely different from our natural world is created.
Although scenes and actions similar to reality are designed in animated films, it still
falls short of reality. On the one hand, it destroys the audience’s viewing habits, and
on the other hand, it provides a brand new aesthetic experience (Herhuth 2015, p.
32). This sense of gap with reality is the reason why animation films are so
fascinating.
 As one of the best studios in the animated film market, Pixar is most
commended for its storytelling skill, which relates to the choice of subject matter,
character setting and script content. Zornado mentioned in a monograph on Disney:
“Though representations of race, gender, and class have evolved since the first
iterations of the golden era, Disney fantasy-as-ideology speaks to the unconscious
as it manifests itself as language, discourse, culture, and social practice (Zornado
2017, p. 175)”. Being a part of Disney, Pixar is no exception. The tolerance for
diverse subjects makes it also described as “social, political, linguistic, cultural, and
economic crossroads (Fisher Fishkin 2005, p. 22)”.
 Pixar has also received a few criticisms on the subject matter. Keith M. Booker
once criticised A Bug’s Life (1998) in his book Disney, Pixar, and the Hidden
Messages of Children’s Films (2010), proposing that the concept conveyed by this
film is too old and conservative (Booker 2010, p. 82). However, most researchers
and audiences still identify that Pixar’s stories are highly innovative, artistic and

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independent. Many non-fairy tale backgrounds avoid the appearance of cartoons
and try to portray adult characters with adult problems (Meinel 2016, p. 10); these
breakthroughs have made Pixar more special.
 The diverse creative themes have also enriched the various types of research on
Pixar. The values conveyed behind the selected topics are the main discussion and
study targets. The research subject areas cover media studies, psychology, social
sciences and pedagogy; most of these studies focus on the influence of Pixar on
children’s behaviours and values. Booker revealed the hidden impact of animated
films on children’s matters, including political propaganda (Booker 2010, p. 137).
The cultural information hidden in Pixar’s animation has many research values on
religion, race, and gender. Cheu (2013) had a comprehensive discussion in the book
Diversity in Disney films. In 2016, Rosa discussed the worldview presented by the
Disney’s language and accent. In terms of gender issues, Decker (2010) studied the
characterisation of gender in Pixar movies, and Ebrahim (2014) noticed the gender
awareness in Pixar movies and its influence on children’s gender perception.
Hofmann (2018) also found that Pixar films are very suitable as teaching materials
for non-native English-speaking children to acquire English because of the cultural
connotations contained in films. In addition to the output of values, Tranter and
Sharpe (2021) discussed the relationship between Disney-Pixar movies and
children’s sports and children’s independent development.
 In the existing research, these fruitful research results primarily focused on
children, and although they mentioned adults, mainly for parents or teachers at the
educational scene. “[Disney-Pixar Film’s] relationships with adult audiences are
underappreciated and under-researched. (Mason2017, ix)”, there is little research
on the analysis and exploration of general adult audiences. The gap between
children and adults is what this thesis intends to fill.

Death in Animated Films
Perhaps it is animated films’ characteristics; many studies directly referred to them
as children’s films. The research has focused on discussing the potential impact of
animated films on children’s development. When making animation, Disney’s
initial target audience was adults (Madej and Lee 2012, p. 70). However, with the
characters’ pleasing appearance and exciting stories, it was also popular between
teens and children. As the customer base expands, Disney has adjusted some of
these details indeed. However, it still believes that animated movies can cross all
boundaries, whether age, finances or countries (Madej and Lee 2012, p. 70), rather
than serving specific target audiences. All in all, Pixar does not restrict its choice of
subjects, nor deliberately hide or avoid particular topics, and always believes that
everyone can dive into stories and enjoy them.

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Death has never been absent in the film narrative (Niemiec and Schulenberg
2011, p. 388). The study on death in animated films and the exploration of its impact
on children have around 20 years of history. In 1999, Sydney M. A. observed
children’s grief narratives with four popular movies at the time. She found that
through animated films, children can effectively accept the concept of death as an
eternal disappearance and can ease or guide them to express their sad emotions.
While young audiences watch animated movies that contain death scenes, they
gradually accumulate their understanding of death and master potential coping
skills simultaneously. Additionally, if parents or adult audiences can discuss with
them, the discussion can release their sense of sadness and pain about the movie’s
plots.
 Following this study, Cox, Garrett, and Graham proposed complete research in
2005. They selected ten Disney movies includes scenes of death, and scored these
death scenes according to the below five coding criteria:

  Character status: protagonist, antagonist, side characters;
  Depiction of death: explicit, implicit, sleep;
  Death Status: permanent/final, reversible-same form and reversible-al-
 tered form death;
  Emotional reaction: positive, negative, lacking emotion;
  Causality: purposeful, justified, unjustified

 This study pointed out that the most significant difference between adults and
children lies in comprehending the irreversibility of death. Children have
established the understanding of death by seeing death scenarios; the concept helps
them know what is sad, why they feel sad and realise that it is normal to feel sad,
down, and other negative emotions.
 After Cox et al. proposed this study in 2005, this topic has been silent for a
while. Until 2017, Tenzek and Nickels expanded the research results. They
upgraded the research scale from ten to 57 movies, focused on Disney’s films, and
contained Pixar’s works because Disney acquired Pixar in 2006. This large-scale
study found that variously portray death information helps kids solve their
impression of death. More importantly, they were more willing to share and discuss
death because of a reduced sense of fear.
 Additionally, they found that the depiction of death scenes in animated films
has changed, and there is a tendency to strengthen the description of the
psychological level (Tenzek and Nickels 2017, p. 61). Especially when facing
relatives’ death, there are many complex emotions, such as sadness, anger, regret
and nostalgia. Moreover, animated films have added more comforting and
empathetic content. These supporting behaviours can calm the anxiety caused by
death’s persistence (Cicirelli 2002; Niemiec and Schulenberg 2011 cited in Tenzek

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and Nickels 2017, p. 62). The study also indirectly proves that animated films can
be considered social support and have an important role.
 In 2018, Graham, Yuhas and Roman added discoveries to this research field.
Their research materials include Disney and Pixar movies and are divided into two-
time segments to compare and observe the differences. The first part is ten selected
films from Disney Films launched between 1937 and 2003, and the other part is
eight selected Disney/Pixar Films released from 2003 to 2016. Besides adjusting
the material collection, they also added one more to the five classification criteria
(character status, depiction of death, death Status, emotional reaction, causality)
from Cox et al. proposed in 2005:

  Coping Mechanisms: positive coping skills, negative coping skills

 Because the role’s response can prove which coping mechanisms are positive
and effective and which mechanisms are negative and unhelpful, these characters
with positive coping skills can serve as role models for children to cope with death
in their own lives (Corr 2010, p. 34). The study also echoes with Tenzek and
Nickels (2017); the specific active coping mechanism in the films, including
obtaining the support of friends and relatives, expressing oneself sadness
appropriately, and achieving goals, have all made death negative emotions and
favourable implication. Nevertheless, the importance and value of family members
are more prominent.
 What is different from the previous ones is that they found that death in Disney
and Pixar animated films has gradually gotten complicated. Disney and Pixar have
more content in their new movies than Disney’s classic animated films to conceal
the persistence and irreversibility of death. However, these implicit contents do not
prevent the audience from realising the fact that death has occurred. In other words,
these hiding messages can also be regarded as a novel way of death depiction. Their
comprehensive analysis also shows that modern Disney/Pixar movies positively
impact children’s understanding of death.
 Bridgewater, Menendez and Rosengren published their study in 2021. They
selected 50 American animated films from the popular animated films ranking list
released between 1970 and 2016 and had an incredible box office performance.
Previous research also proved that death scenes are typical content in those
animated movies, not just features in Disney and Pixar movies. Additionally, most
death scenes were created in an accurate biological manner, in line with real life.
 In this study, Bridgewater et al. focused on exploring parents’ primary
communication roles between animated films and children. They found that parents
often avoid the topic of death. When kids proposed questions about death, they
preferred answering questions with “It is (just) a movie (Bridgewater et al., 2021,
p. 24)”. These non-direct and non-specific responses would not help children
understand. This phenomenon showed that parents did not take it seriously, and on

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the other hand, they had no idea to give a proper explanation. Although animated
movies are not the most suitable medium for life education (Bridgewater et al. 2021,
p. 25), it is still essential to use them as the key to open the topic. However, many
parents did not regard movies as a communication medium.
 From these previous studies, it can be considered that the research on animated
films mainly revolves around children. Nevertheless, they also mentioned that
although adults can understand the concept of death, sometimes adults and parents
are even more reluctant to take the initiative to talk about death (Cox et al. 2005, p.
279). Most of the parents did not care how death is portrayed in children’s films.
Among the parents who noticed the death scene, they “tended to discuss
misconceptions present in films, tended to provide more non-functionality
information, and tended to not just say that the death is not real because it happened
in a movie. Additionally, parents that discussed misconceptions tended to talk more
about afterlife beliefs and say that the death was just in the movie (Bridgewater et
al. 2021, p. 23)”.
 Animated films have become a buffer and medium for discussing this issue.
Because of the movie’s plot, death represents the disappearance of life and often
contains meaning to life. For example, although good people would die, they would
be remembered by someone (Cox et al. 2005, p. 276). People should not regard
death scenes as merely an output of negative emotions, “Through engaging in EOL
(end-of-life) conversations, we can reconstruct the taboo nature of death and die
into something more positive (Tenzek and Nickels 2017, p. 63)”. Tsay and
Krakowiak (2011, p. 11) found that meaningful movies remind viewers of death,
and more specifically, they cause a greater extent of sadness, which is related to
thoughts about death. However, these films also enhanced a higher level of
emotional cognition. For example, the film’s death scenes help the audience
increase their sensitivity and compassion to the dead and survivors (Shapiro and
Rucker, 2004, p. 447). Sometimes, adults are unable to deal with emotion-related
issues maturely. Therefore, how non-child viewers watch and think about death
scenes is worth exploring, and this is the space part that has not yet been developed
in previous studies.
 In these previous studies, researchers also revealed a desire to modify the
research methods by coding movie scenes according to specific classification
criteria and analyse them through statistics. This coding analysis is time-consuming
and laborious because each movie has to be scored by two or more professional
researchers. This method brings several problems: the first is the workforce
uncertainty; even though they are professionals, they still cannot be completely
objective. Furthermore, with more and more implicit death scenes and dialogues,
the difficulty of judgment gets higher. Second, it is hard to enlarge sample numbers
because the more samples, the longer viewing and scoring time. These two issues

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hinder researchers from making long-term observations of Disney and Pixar’s
animated films.
 On top of that, it is also unfriendly to study the works of other animated studios.
If coverting the existing analysis method to sentiment analysis, coding work can be
omitted. Researchers only need to clean up the script files and focus more on
analysing the characters’ emotional reactions in different scenes.

Theoretical Framework
This section introduces the main concepts of the emotion theory and then sorting
out its development and importance; afterwards explaining Plutchik’s wheel of
emotions and its relationship with sentiment analysis.

Emotion Theory
As more and more researchers have adopted sentiment analysis, it is not enough for
researchers to divide emotions into four in-depth analysis of the text, and emotion
modelling combined with emotion words came into being (Almashraee, Monett and
Paschke 2016, p. 7). The concept of emotion modelling is to use ontology to provide
the expressive relationship between emotions. It can make the calculation of
emotions more practical because it contains the most common and known emotions.
The subject of emotion theory is broad and diverse. Kim and Klinger (2019)
compared three emotional viewpoint models: Ekman’s basic emotional theory,
Plutchik’s emotional wheel and Russel’s winding model. After analysis, the
structural model proposed by Plutchik can best meet the analysis needs, which has
become the central theory of sentiment classification.
 Plutchik’s wheel of emotion distributes emotions in different positions on the
turntable and comprises eight basic bipolar emotions: joy and sadness, anger and
fear, trust and disgust and surprise and anticipation. As the following Figure 1,
when moving to the centre of the steering wheel, every primary emotion is as the
intensity increases; the emotions that are closer to the axis are easier to recognise;
on the contrary, the farther away from the centre represents hard to aware.

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Figure 1. Plutchik’s wheel of emotions

Source: Kim and Klinger (2019), p. 4

 Smith and Schneider (2009, p. 583) have criticised Plutchik’s wheel of
emotions for not having enough experience support. Nevertheless, it is hard to
rebuild a model by creating a new one that integrates all emotions. Therefore, this
theory still highly accepted and appreciated in text analysis.

Methodology
The chapter explains the analysis method and process. First, it introduces the
research method and the analysis tools used. The next part will present the data
sources and demonstrate how to collect and analyse them in straightforward steps.
At the end of this chapter, the part discusses the ethical issues in data collection.

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Analysis Method: Sentiment Analysis
Nasukawa and Yi (2003) initially proposed the term sentiment analysis, and the
term opinion mining first appeared in the study of Dave, Lawrence and Pennock in
2003. Sentiment analysis can be summarised as a type of NLP. The beginning of
NLP is in the 1950s; it is an interdisciplinary subject of artificial intelligence and
linguistics (Nadkarni, Ohno-Machado and Chapman 2011, p. 544). The purpose is
to explore how to use computers to understand and manipulate natural language
text or speech to do valuable matters (Chowdhury 2003, p. 51).
 In recent years, sentiment analysis has become a significant study with the
development of social media. These unprocessed subjective opinions become
essential information about social events, political movements, company strategies,
marketing activities, and product preferences. As a result, it has attracted the
scientific community’s attention and the business community (Cambria et al. 2013,
p. 15).
 Liu provided a complete definition and scope of tasks in Sentiment Analysis
and Opinion Mining (2012):

 Sentiment analysis, also called opinion mining, is the field of study that analyses people’s opin-
 ions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as prod-
 ucts, services, organisations, individuals, issues, events, topics, and their attributes. It represents
 a significant problem space. There are also many names and slightly different tasks, e.g., sen-
 timent analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis,
 effect analysis, emotion analysis, review mining, etc. However, they are now all under the um-
 brella of sentiment analysis or opinion mining (Liu 2012, p. 1).

 The critical value of sentiment analysis is that, unlike factual information,
emotions and opinions have essential characteristics, namely subjectivity, and
subjectivity comes from many sources (Liu 2010, p. 627). A single opinion is very
subjective and usually not enough to take actions. Still, when gathering multiple
people’s opinions, it represents a shared experience, so how to summarise in some
forms become necessary (Hu and Liu 2004, p. 168).
 According to Medhat, Hassan and Korashy (2014, p. 1094), sentiment analysis
mainly has two analysis approaches: lexicon-based techniques and machine-
learning-based techniques, as the structure shows in Figure 2. This thesis utilises
the lexicon-based approach to calculate results using sentimental words and phrases,
and it is the earliest used sentiment analysis technology. The technology can be
subdivided into two methods: dictionary-based method and corpus-based method.
In the former type, emotion classification is carried out using dictionaries, such as
the terms in SentiWordNet and WordNet, and the NRC Word-Emotion Association
Lexicon used in this thesis. Corpus-based sentiment analysis does not rely on
predefined dictionaries but statistical analysis of the document collection content
(Dang, Moreno-García and De la Prieta 2020, p. 482).

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Figure 2. Sentiment Analysis methods

Source: Medhat et al., 2014

 This thesis adopts the lexicon-based approach, which has been proven to
perform well in many fields (Liu 2012, p. 50). Analysing emotions is to
conceptualise emotions. Such exploration helps us understand the writer’s true
consciousness (Goatly 2011, p. 13): this is because the language that people express
is not necessarily the same as the concept they want to express, and this depends on
the affective grounds (Goatly 2011, p. 25), which is why sentiment analysis is
crucial. The establishment of an emotional dictionary is a system for identifying the
emotions contained in words. The purpose is to build a collection of fundamental
phenomena and specific core formal theories for abstract languages (Hobbs and
Gordon 2011, p. 27). In this way, we can better understand the core meaning behind
languages.
 According to Ding, Liu and Yu (2008, p. 234-235), sentiment analysis
processing sentiment in the text can be divided into four sub-steps:

  Mark sentiment words and phrases: Mark all sentiment words and
 phrases in the sentence. The sentiment score assigned to each positive
 word is +1, and the sentiment score given to each negative word is -1, or
 neutral.
  Apply sentiment shifters: Identify words and phrases that can change
 emotional orientation, especially for identifying negative comments.
  Handle but-clauses: Indicate opposite words or phrases require special
 treatment because they also often change emotional orientation, such as

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but, however.
  Aggregate opinions: In this step, the opinion summary function is ap-
 plied to the obtained sentiment scores to determine the final orientation
 of each aspect of the sentiment in the sentence.

Sentiment Analysis in Humanities
The method of text research has turned to the digital analysis method as the primary
research mode (Rockwell 2003, p. 209). As one of the text analysis methods,
sentiment analysis is widely used in various fields of research. In computer science,
the leading research discussed building more accurate models and using machine
learning to train more precise analysis programs. The main point concerned in this
thesis is how to apply sentiment analysis to the research in the humanities field as
a text analysis method. Therefore, this section will focus on the literature review in
humanities.
 Sentiment analysis is used in various humanities situations, in literature, from
novels, poems, lyrics, scripts and other different applications. Kim and Klinger
(2019) used sentiment analysis to summarise many humanistic research situations,
including emotion classification, story ending prediction, genre and story-type
classification, temporal change of sentiment, character network analysis and
relationship extraction, emotion flow analysis and visualisation and hybrid analysis.
 In addition to analysing a single text or a specific number of texts, large-scale
database analysis and research have also appeared. For example, in 2015, Sprugnoli
et al. used sentiment analysis to systematically analyse historical texts and corpora.
From this, they had a deep understanding of the historical archives and the
viewpoints of analysing historical documents and planned to continue to using
sentiment analysis to track views of specific topics over time.
 Furthermore, many researchers have begun to modify and create emotional
dictionaries and emotional lists to align with the humanities. Barros, Rodriguez and
Ortigosa (2013) used the four words of joy, anger, fear and sadness as emotional
keywords to research poetry and build an emotional dictionary for Spanish based
on their analysis results.
 Mäntylä, Graziotin and Kuutila (2018) found that the number of papers using
sentiment analysis proliferates every year, containing many published papers in the
humanities field. However, although the humanities field has a high interest in
predicting and analysing emotions, there is not much change in the operation of
analysis tools. Humanities scholars’ concern about what sentiment analysis is and
how to apply it in analysing (Kim and Klinger 2019, p. 23), which is entirely
different from the direction and goal of computer science and computational
linguistics on how to adjust programs to improve recognition and make analysis
more automated.

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Although sentiment analysis has become increasingly popular in humanities, it
mainly focuses on studying and researching literary texts and historical documents.
The analysis of critical content is still relatively rare. The sentiment analysis of
movie reviews is a common task in computer science. In 2009, Yesenov and
Misailovic verified that movie reviews help understand the attitude and satisfaction
of movies; Jain (2013) also found that movie reviews can predict box office.
Therefore, this thesis tries to use sentiment analysis to organise and explore scripts
and reviews.
 This thesis focuses on analysing results rather than testing and improving the
sentiment analysis system. The next chapter will explain how appling this analysis
theory in detail.

Data Analysis: Distant Reading
This thesis adopted a quantitative text analysis, and it is based on the perspective of
distant reading, which is a method for using computer technology to engage in text
analysis. Franco Moretti, the initiator of The Center for the Study of the Novel at
Stanford University, proposed distant reading in his artical Conjectures on World
Literature in 2000; it is a brand-new approach compared to the traditional close
reading. He applied the network theory in sociology to plot analysis (Moretti 2011).
He found that distant reading may provide a more abstract analysis of texts and
process single text or massive texts. Moretti described the definition of this method
in the book Distant Reading (2013):

 Distant reading: where distance, let me repeat it, is a condition of knowledge: it allows you to
 focus on units that are much smaller or much larger than the text: devices, themes, tropes—or
 genres and systems. And if, between the very small and the very large, the text itself disappears,
 well, it is one of those cases when one can justifiably say, less is more. If we want to understand
 the system in its entirety, we must accept losing something. We always pay the price for theo-
 retical knowledge: reality is infinitely rich; concepts are abstract, are poor. But it is precisely
 this “poverty” that makes it possible to handle them, and therefore to know. This is why less is
 more (Moretti 2013, p. 48-49).

 Although the analysis method of distant reading inevitably sacrifices some
intricate parts of the texts, it is constructive for processing large and even huge
amounts of texts so that analysts may not fall into the predicament of “not seeing
the wood for the trees”. However, as with every existing theory, there are still many
different voices in academia.
 In the article Literature is not Data: Against Digital Humanities, Stephen
Marche (2012) mentioned that literature is not the same as data, so it is meaningless
to read it in the form of data analysis. Treating literature as data causes literature
incomplete, and literary works may lose their literary sensation and aesthetics. He
believes that calculating and comparing of part of speech and vocabulary frequency
through formulas ignores the essence of literature and cannot obtain effective and

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valuable results. Moreover, this kind of extensive understanding of texts is not
helpful for the readers to realise the texts’ meaning more deeply.
 “Sometimes complexity is necessary.” Maurizio Ascari (2014, p. 4) said.
Because although distant reading magnifies the concept of text architecture and
allows researchers to pay more attention to contextual relevance, it also fragments
the text. These fragments do not necessarily help the readers more organised when
reading and even makes the process more “dirty” and “messy” (Ascari 2014, p. 4).
 Of course, some scholars have put forward further critical thinking in response
to the opposing views. Paying attention to the layout of texts makes the results of
the distant reading analysis neutral and ambiguous. Still, there is also the
characteristics that close reading lacks which overemphasises and pursues
refinement (Syme and Selisker 2012). There is an increasing trend in research that
adopted distance reading as a research theory and method, and it is related to
researchers’ practical problems. With the development of history, the number of
texts increases gradually, and researchers may not cope with the whole amount of
data accumulated in the database (Underwood 2014, p. 64). This practical reason
makes distance reading more and more popular. However, it does not deny close
reading, but the two methods deal with different literary levels, and it is crucial and
necessary to adopt comprehensive approaches (Ascari 2014, p. 15).

Analysis Tool
Python 3.7 is the main program for performing sentiment analysis in this thesis. It
can process the VADER toolkit and classify the sentiment of the text content into
four indexes: positive, negative, neutral, and compound. Moreover, it can operate
the NRC Word-Emotion Association Lexicon to convert these sentiments into eight
basic emotions: anger, anticipation, joy, trust, fear, surprise, sadness and
disgust.The following paragraphs explain in further detail.

Python
Python is a widely used high-level programming language. Guido van Rossum
established the first version in 1991, and the lastest version is Python 3.9.41. It is a
general-purpose programming language without designing for particular purposes.
Its flexibility is one of the main reasons why Python is popular. Python’s design
philosophy emphasises readability and conciseness because this characteristic
makes it not only extensively used for rapid application development but also
frequently used to process data on social platforms (Rossum 2009) 2.

1 Python > About [2021-04-10].
2 Rossum, Guido Van (2009-01-14), Python’s Design Philosophy. (blog). [2021-04-10].

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In data analysis, Pandas is used. Pandas combines the features of NumPy
(Numerical Python) and the data manipulation capabilities of spreadsheets and
connected databases (SQL). Furthermore, they can be used to reconstruct, cut,
aggregate, and select subsets of data. Through Pandas, the speed of finding out the
information and meaning in the data gets higher.

VADER
VADER (Valence Aware Dictionary and sEntiment Reasoner), developed by Eric
Gilbert, is a parsimonious rule-based model for sentiment analysis (Hutto and
Gilbert 2014). It is a tool specifically used to analyse emotions expressed in social
media and only supports English content analysis.
 Based on the official developer document by C.J. Hutto, he mentioned what
kind of words would be aware, specially in VADER3:

 • typical negations (e.g., “not good”)
 • use of contractions as negations (e.g., “wasn’t very good”)
 • conventional use of punctuation to signal increased sentiment intensity
 (e.g., “Good!!!”)
 • conventional use of word-shape to signal emphasis (e.g., using ALL
 CAPS for words/phrases)
 • using degree modifiers to alter sentiment intensity (e.g.,
 intensity boosters such as “very” and intensity dampeners such as “kind
 of”)
 • understanding many sentiment-laden slang words (e.g., “sux”)
 • understanding many sentiment-laden slang words as modifiers such as
 “uber” or “friggin” or “kinda”
 • understanding many sentiment-laden emoticons such as :) and :D
 • translating utf-8 encoded emojis such as and and 
 • understanding sentiment-laden initialisms and acronyms (e.g., “lol”)

NRC Word-Emotion Association Lexicon
According to the NRC Word-Emotion Association Lexicon published by
Mohammad and Turney in 20134, this is based on Plutchik’s eight basic emotions
(anger, fear, anticipation, trust, surprise, sadness, joy and disgust) to annotate the
emotions of words dictionary. This dictionary used Roget’s thesaurus as the source
of terms; the thesaurus divides related words into approximately one thousand
categories.

3 Github > cjhutto > vaderSentiment [2021-04-12].
4 Saif M. Mohammad > Lexicons > NRC-Emotion-Lexicon [2021-04-12].

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