INVESTIGATING GAME DEVELOPERS’ GUILT EMOTIONS USING SENTIMENT ANALYSIS

International Journal of Software Engineering & Applications (IJSEA), Vol.9, No.6, November 2018
DOI:10.5121/ijsea.2018.9604 41
INVESTIGATING GAME DEVELOPERS’ GUILT
EMOTIONS USING SENTIMENT ANALYSIS
Lamiaa Mostafa and MarwaAbd Elghany
Department of Business Information Systems, College of Management and Technology,
Arab Academy for Science & Technology, Alexandria, Egypt
ABSTRACT
Game Development is one of the most important emerging fields in software engineering era. Game
addiction is the nowadays disease which is combined with playing computer and videogames. Shame is a
negative feeling about self evaluationas well as guilt that is considered as a negative evaluation of the
transgressing behaviour, both are associated withadaptive and concealing responses. Sentiment analysis
demonstrates a huge progression towards the understanding of web users’ opinions. In this paper, the
sentiments of game developers are examined to measure their guilt’s emotions when working in this career.
The sentiment analysis model is implementedthrough the following steps: sentiment collector, sentiment
pre-processing, and then machine learning methods were used. The model classifies sentiments into guilt or
no guilt and is trained with 1000 Reddit website sentiment. Results have shown that Support Vector
Machine (SVM) approach is more accurate incomparison to Naïve Bayes (NV) and Decision Tree.
KEYWORDS
Ethics,Game Addiction, Guilt Emotions, Software Engineering, Sentiment Analysis Model& Value Sensitive
Design
1. INTRODUCTION
Ethics is the study of value concepts such as ‘bad,’ ‘good,’ ‘ought,’ ‘right,’ ‘wrong,’ put on into
actions linked to group standards and norms. Hence, it is contended with many issues essential to
practical decision-making (Veatch, 1977). Computer software systems stay at the centre of
modern decision making, counting data/information storage and manipulation, data availability,
and ‘alternatives’choice and formulation (Schmoldt and Rauscher, 1994). The ubiquity of
software systems in every aspect requires that the created environment should be critically
investigated as they are developed and deployed as well. A range of artificial intelligence (AI)
approaches has been proposed to represent different codes of ethics in environmental decision
systems (Thomson, 1997).
Artificial Intelligence (AI) empowers modern information technologies and the advent of
machines and have already intensely impacted the world of work in the 21st century. Computers,
algorithms and software actually streamline everyday tasks, and it is difficult to conceive how
tasks could be accomplished without their use. The name behind the idea of AI is John McCarthy,
who started his research in 1955 and suggested that each aspect of learning and any other domain
of intelligence can specifically be defined in details then simulated by a machine. Therefore, the
term ‘artificial intelligence’ entails the investigation of intelligent problem-solving behaviour to
generate intelligent computer systems.There are two kinds of artificial intelligence: weak artificial
intelligence; where intelligence is stimulated by the computer for examining cognitive processes;
strong artificial intelligence;where computers are able to perform intellectual, self-learning

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processes by the right software/programming for the optimisation of their behaviour based upon
experience.This involves automatic networking with other machines leading to a dramatic scaling
effect (Wiss kirchen et al. 2017).
The term “ethics” tackles any intentional action affecting others’ values and lives either positively
or negatively. Software engineering is mainly perceived as a technical discipline for the
development of software. The industry personnel who contribute in professional software
developmentare primarily called as information systems analysts and the key concern of the
software engineering activity is focused on the technical appropriateness of the developed
products. Reed (2000) demonstrated that roughly more than one billion people around the world
rely on software systems for the conduction of their daily lives, which has made many in the
computing field to consider the nontechnical aspects and the ethical effect of their daily decisions
and the values enclosed therein. The relation between ethics and computers can be clearly viewed
when computers are affected by humans’ decisions, and then people’s lives are impacted by those
decisions. Accordingly, human values are connected to technical decisions in this manner, which
are made by computing professionals including the individuals, teams, or the management during
the design, development, construction and maintenance of computing artefacts influencing other
people. For example, the airbag design release William pactothers’ lives and should be directed
by an ethical decision about human values.
This paper begins with an overview of the perception of ethics in computer software systems and
its associated activities in addition to its related technical decisions made by professionals.Then
focuses on the game addiction and the accompanied guilt emotions that the developers can feel
about it through the collection of Reddit responses subsequently, analysing these responses using
the sentiment analysis model to classify whether they are guilty or not.
2. BACKGROUND
2.1. SOFTWARE ENGINEERING ETHICS
The role of ethics in software system design has recently fostered in significance.Mason (1986)
demonstrates an early viewpoint on ethical issues in the information age,classifying them into
accessibility, accuracy, privacy, and property concerns.Later on, more views added concerns
about knowledge use in organizations (Bella,1992) and its affecting impact on life quality
(Forester and Morrison, 1994). The issue is not about malicious be haviour,such as computer
crime, deliberate invasions of privacy, hacking, software theft, and viruses, rather than the subtler
of the impact that the software development and deployment can have on people and their
cultural, corporate andother institutions. Previous approaches to system development were
concentrating towards technical aspects, while current approaches give emphasis to the
consideration of all aspects of the human environment in which the systems are being developed
and used.
Software engineering ethics can be overviewed from three approaches:first, it canbe regarded as
the activity of software engineers who are making practical choices that have an effect on other
people in substantial ways. Second, it can act as a description of a collection of
principles,guidelines, or ethical imperatives giving guidance or legislative action.And third, it can
be named as a discipline, which investigates the relationship between the other two senses of
ethics.Accordingly, software engineering ethics is considered as mutually principles’ set and
activity as well (Gotterbarn et al., 1997).

43
The ethical activity embedded in technical decisions should be dependent on an understanding of
the effect of those decisions. The technical decisions should be consciously steered by
values.Professions designate the values in Codes of Ethics, Codes of Conduct and Codes of
Practice, where these codes help in many ways embracing guidance for the ethical selections of
the practitioner and enlightening the public and the practitioners about the profession’s ethical
standard. A good software engineering decision entails an awareness of both the technical and the
ethical dimensions.Codes of ethics are statements about the interconnectivity between values and
technology to ensure that the activities of practicing professionals care for human values rather
than do any harm to them.The discipline of software engineering ethics examines the relationship
between technology and the ethical principles; how it influences society, citizens, and products
(Gotterbarn, 2002).
In the nineties, a number of organizations like the Association for Computing Machinery (ACM),
the Australian Computer Society, the British Computer Society (BCS), the IEEE Computer
Society (IEEE-CS), and the New Zealand Computer Society have reconsidered their ethics’codes
to state explicitly an ethical obligation to the public (Gotterbarn a, 1996). Especially, the IEEE
Computer Society joined by the ACM started to professionalize software engineering and set
initial goals in the following:
1. Adoption of Standard Definitions
2. Definition of Required Body of Knowledge and Recommended Practices
3. Definition of Ethical Standards
4. Define Educational Curricula (Gotterbarn b, 1996).
However, the above goals are coherent with the goals of any other professions because the key
element in any profession stands in its recognition of ethical and moral responsibilities to its
clients, to society, and to the profession itself. Nevertheless, many professions such as medicine
for example declare these responsibilities publicly in a code of ethics then
afterwardsnecessitatetraining in professional ethics for entering the profession. The
responsibilities formulated in the Software Engineering Code of Ethics and Professional Conduct
(SECODE 1999) were briefed in:Software engineers shall be committed to these Eight Principles:
1 PUBLIC - act steadily with the public interest.
2 CLIENT AND EMPLOYER - perform in a way, which is in the best interests of their client or
employer.
3 PRODUCT -make sure that their products and related modifications fulfil the highest
professional standards possible.
4 JUDGMENT - keep independence and integrity in their professional judgment.
5 MANAGEMENT - endorse an ethical approach to the management of software development
and maintenance.
6 PROFESSION - align the reputation and the integrity of the profession in consistence with the
public interest.
7 COLLEAGUES - being fair and supportive to their colleagues.
8 SELF - contribute in lifelong learning in regards to the practice of their profession.

44
The Software Engineering Institute recognised software engineering as an engineering form
applying the principles of computer science and mathematics for the achievement of cost
effective solutions to software practical problems in the mankind service. The perception of cost
effectiveness denotes the resources investment for good value satisfying the required quality and
minimised time (Ford, 1990).Even though, software engineering does not meet all of the above
mentioned inscriptions of a profession, they are still important to understand how ethics is linked
to software engineering. Training and specialised skills are spent in the developments of products
that have a direct impact on many lives for their usage in society, in other words affecting the
public's well-being. Consequently, software engineering ethics can be identified as a professional
ethics(Pour, 2000). Like medical professionals, software engineers should be aware that
knowledge is commonly inaccessible to people outside the profession and have special duties and
responsibilities to their clients like lawyers, which can be inconsistent with duties and
responsibilities towards the society at large. Then software engineering ethics shares the common
base with other professional ethics for health, safety, and welfare concern. How ever,it is uniquely
bounded to the technical details of computing (SE Code, 1999).
The context to which moral rules are applied differentiates between various professional ethics
including legal, medical and engineering ethics. For the reason that different contexts raise
different ethical concerns, even the order in which they are practised changes for each application
domain. Within the software engineering process, moral rules are assigned different importance at
different phases of the software development life cycle. An understanding agreement (informative
consent) creates a rule during the requirements phase of life-critical software. The honesty and
transparency principle is another rule during testing. Yet the tort law should be confused with
professional ethics in the sense of individual responsibility. If a developed product does not
encounter the ideological technical goals of the profession there is no need for legal action till
some damage has happened. There exists three main requirements for a tort law suit: (1) the duty
is being indebted; (2) the duty is penetrated; and (3) caused damages happened by the duty
penetration, one could be unethical and still the law was not violated(West 1991).In the design
phase, other moral rules may get exercised first.
For example; in case of a journal file design for a library check-out system to ascertain the status
of books and the number of additional copies to be ordered, if required. The association of the
patron's name with the book checked out has a potential for the violation of several moral rules,
such as the violation of privacy and the deprivation of pleasure since one does not feel free to read
what the other one wants, and perhaps causing psychological pain. Moreover, the language
selection for a life-critical system can have moral implications because if the language is difficult
for debugging, writing, or modification, then people are put at risk and principles of good system
design in that language selection are violated. The ethical issues that can be drawn here;software
developers were morally culpable to take a project beyond their skill of language proficiency,
despite their awareness of the major difference in languages, they chose to stick to the more
dangerous language for other purposes such as profit, then they have intentionally violated
professional ethics.
Technical responsibility encompasses moral commitments to standards of software production, to
the software development process whereas societal responsibility implicates commitment to the
usage of these specialised skills in the society service.Software development is considered as a
social process and the software engineer has a two-fold compulsion for excellence striving. One
compulsion depends on the fulfilment of technical standards and the other one is about social
responsibility to those end-users who will work with the developed product. These ethics’
approaches provide positive guidance for software practitioners’ behaviours (Gotterbarn, 1999).
Without an ethical approach, systems might be put into operational use regardless of persistent

45
errors and faulty trials that is why it is not surprising that software developers hardly afford their
clients or purchasers with warranties of any substance (Forester and Morrison, 1994). On the
other hand, it is up to the end-user in most cases to provide critical feedback and the availability
of software websites and e-mail contact addresses offer avenues for end-user responses to
unethical practices.
A number of books have been printed on computer ethics (also denoted to as machine morality).
A hypothetical theoretical scenario is delineated in the book of "Moral Machines" where an
artificially high oil price is contributed to "unethical" robotic trading systems causing the
automated program to regulate the energy output switches over from oil to more polluting coal
power plants in order to prevent the rise in electricity prices. At the same time, coal-fired power
plants cannot withstand flowing at full production long and blasts after some time leading to
massive power outage with the concerns it has for life and health. Nevertheless, power outages
produce terror alarms at the nearest international airport resultant in chaos both at the airport and
arriving jets colliding etc. The outcome is loss in lives and huge economic costs, merely for the
reason of the interaction between independently programmed systems that automatically make
decisions (Wallach et al., 2009).
The scenario illustrated above shows that it is significant to have control mechanisms when a
multisystem, which are making their own decisions interact to set limitations, and give
notification to the operators about the condition deemed to call for human review. Therefore, it
becomes important that moral based decision-making becomes a part of the artificial intelligence
systems. The ethical implications of the possible actions that systems can take must be evaluated
and this can be done on several levels, including if private laws are broken or not. However, it is
not easy to construct machines that are incorporating all the world's philosophical and religious
traditions. Furthermore, a very effective driver support system can be developed to reduce the
number of accidents and save numerous lives yet, it is not desirable to be responsible for building
a system where there is a real risk for severe adverse events in case of failure (Tørresen, 2014).
In the same book detailed review of how artificial moral agents can be applied is provided. This
embraces the use of ethical expertise in program development. It suggests three approaches: logic
and mathematical formulated ethical reasoning, machine learning methods depending on
examples of ethical and unethical behaviour and simulation where it is viewed by different ethical
strategies. For example; in case of a loan application to a bank, an AI-based system can be used
for credit evaluation based on a number of criteria. If the application is rejected, the reason behind
refusal should be clarified. Skin color or race can be considered one of the parameters included
rather than the applicant’s financial situation, still the bank can not state it explicitly. A more open
system for inspection can however, show that the residence address has been crucial in this case.
It has given the same result that the selected criterion has been provided. This is essential to avoid
as far as possible through the simulation of AI systems’ behaviour of AI systems for the detection
of possible ethically undesirable actions.All software that will substitute human evaluation and
social function should follow criteria such as accountability, inspect ability, manipulation
robustness and predictability. Also, all developers should have an inherent desire to develop
products delivering the best possible user experience and user safety. The capability for
manipulation must be restricted, and the system must have a predictable behaviour. It is definitely
easier for a robot to move in a known and limited environment rather than in new and unfamiliar
surroundings (Wallach et al., 2009).
The Italian robot scientist Gian Marco Viareggio introduced the term roboethicsin 2002. He
realised a need for guidelines for the development of robots intended to help progressing of the
human society and individuals and prevent abuse against humanity. Accordingly, ethics for robot
designers, manufacturers and users are needed. Robots are mechanical systems that might

46
Unintentionally cause harm to users. There exists a danger that the collected information can be
accessed by unauthorized people and be made available to others through the Internet. Robots can
be misused for criminal activities such as burglary. Although it is presumed that the developers
for robots and AI have good intentions. The intelligent systems must be designed so that the
robots are friendly and kind, while difficult to abuse for potential malicious actions in the future.
In 2004 the first international symposium on robotics was held in Sanremo, Italy. The research
program ETHICBOTS has been funded by EU where a multidisciplinary team of researchers
participated to identify and analyse techno-ethical challenges in the integration of human and
artificial entities. European Robotics Research Network (Euronet) in 2005 provided fund to the
project Euronet Roboethics Atelier, with the goal of the development of the first roadmap for
roboethics for a systematic assessment of the ethical issues surrounding robot design. The focus
was on human ethics for designers, manufacturers and users of robots.The work ended up with
some recommendations that should satisfied in commercial robots such as (Tørresen, 2014):
• Traceability as in aircraft, robots should have a "black box" to record and document their own
behaviour;
• Identifiability, similar to cars, robots should have serial numbers and registration number.
• Privacy and policy,software and hardware should be used for encryption and password
protection of sensitive data that the robot should save.
The machines should be able to make ethical decisions using ethical frameworks. Ethical Issues
are too interdisciplinary that programmers alone should discover them. Researchers in ethics and
philosophy should also be involved in the formulation of ethical "conscious" machines that are
targeted at providing acceptable machine behaviour. Michael And Susan Leigh Anderson have
collected contributions from both philosophers and AI Researchers in the book "Machine Ethics"
(2011). The Book discusses why and how to include an ethical dimension in machines that will
act autonomously. Medical Important information must be reported on, but at the same time, the
person must be able to maintain privacy. Maybe video surveillance is desirable for the user (by
relatives or others), but it should be clear to the user when and how it happens. An autonomous
robot must also be able to adapt to the user's "chemistry" to have a good dialogue.
Value Sensitive Design emphasizes values with ethical import. A list of frequently human values
that might be implicated in systems design is presented in table1 (adapted from Friedman et al.,
2012) to act as a heuristic for values proposition that should be considered.
Table 1. Human Values (with Ethical Import); Source: (Friedman et al., 2012)
Human Value Definition Research
Human Welfare Refers to people’s physical,
material, and psychological well-
being
Leveson [1991]; Friedman,
Kahn, &Hagman [2003];
Neumann [1995]; Turiel [1983,
1998]
Ownership and
Property
Refers to a right to possess an
object (or information), use it,
manage it, derive income from it,
and bequeath it
Friedman [1997]; Herskovits
[1952]; Lipinski &Britz [2000]
Privacy Refers to a claim, an entitlement,
or a right of an individual to
determine what information
about himself or herself can be
communicated to others
Agre and Rotenberg [1998];
Bellotti [1998]; Boyle, Edwards,
& Greenberg [2000]; Friedman
[1997]; Fuchs [1999]; Jancke,
Venolia, Grudin, Cadiz, and

47
Gupta [2001]; Palen&Dourish
[2003]; Nissenbaum [1998];
Phillips [1998]; Schoeman
[1984]; Svensson, Hook,
Laaksolahti, &Waern [2001]
Freedom From
Bias
Refers to systematic unfairness
perpetrated on individuals or
groups, including pre-existing
social bias, technical bias, and
emergent social bias
Friedman &Nissenbaum [1996];
cf. Nass& Gong [2000]; Reeves
&Nass [1996]
Universal
Usability
Refers to making all people
successful users of information
technology
Aberg&Shahmehri [2001];
Shneiderman [1999, 2000];
Cooper &Rejmer [2001]; Jacko,
Dixon, Rosa, Scott, & Pappas
[1999]; Stephanidis [2001]
Trust Refers to expectations that exist
between people who can
experience good will, extend
good will toward others, feel
vulnerable, and experience
betrayal
Baier [1986]; Camp
[2000];Dieberger, Hook,
Svensson, &Lonnqvist [2001];
Egger [2000]; Fogg& Tseng
[1999]; Friedman, Kahn, &
Howe [2000]; Kahn &Turiel
[1988]; Mayer, Davis,
&Schoorman [1995]; Olson &
Olson [2000]; Nissenbaum
[2001]; Rocco [1998]
Autonomy Refers to people’s ability to
decide, plan, and act in ways that
they believe will help them to
achieve their goals
Friedman &Nissenbaum [1997];
Hill [1991]; Isaacs, Tang, &
Morris [1996]; Suchman [1994];
Winograd [1994]
Informed Consent Refers to garnering
people’sagreement,
encompassing criteria of
disclosure and comprehension
(for ‘informed’) and
voluntariness, competence, and
agreement (for ‘consent’)
Faden& Beauchamp [1986];
Friedman, Millett, &Felten
[2000]; The Belmont Report
[1978]
Accountability Refers to the properties that
ensures that the actions of a
person, people, or institution may
be traced uniquely to the person,
people, or institution
Friedman & Kahn [1992];
Friedman & Millet [1995];
Reeves &Nass [1996]
Courtesy Refers to treating people
withpoliteness and consideration
Bennett &Delatree [1978];
Wynne & Ryan [1993]
Identity Refers to people’s understanding
ofwho they are over time,
embracing both continuity and
discontinuity over time
Bers, Gonzalo-Heydrich,
&DeMaso [2001]; Rosenberg
[1997]; Schiano& White [1998];
Turkle [1996]
Calmness Refers to a peaceful and
composed psychological state
Friedman & Kahn [2003];
Weiser & Brown [1997]
Environmental Sustainability Refers to sustaining ecosystems
such that they meet the needs of
the present without
compromising future generations
United Nations [1992]; World
Commission on Environment and
Development [1987]; Hart
[1999]; Moldan, Billharz,
&Matravers [1997]; Northwest
Environment Watch [2002]

48
After the illustration of the software engineering ethical activities and technical responsibilities,
the following sections will discuss game addiction and the use of guilt model to question via
sentiment analysis the guilt emotions of the software developers.
2.2. GAME ADDICTION
Game addiction is considered presently the utmost deliberated psychosocial aspects accompanied
with playing computer and videogames. Lately, the American Medical Association (2007)
intensely stimulated the American Psychiatric Association (APA) to include the ‘‘video game
addiction’’ as a formal diagnostic disorder in the updated revision of the Diagnostic and
Statistical Manual of Mental Disorders (DSM-V, 2012). Adolescents are more exposed to game
addiction than adults (Griffiths & Wood, 2000)because (a)they commonly play computer and
videogames more often than adults(e.g., Griffiths, Davies, &Chappel, 2004). Moreover, there is
substantial disagreement among researchers about the concept of ‘game addiction’.
Game addiction is the widespread dominant term among researchers to designate
compulsive,excessive, obsessive, and mostly problematic use of videogames (e.g., Charlton
&Danforth, 2007; Chiu, Lee, & Huang, 2004;Chou & Ting, 2003; Fisher, 1994; Griffiths &
Davies, 2005; Grüsser, Thale mann, & Griffiths, 2007; Hauge& Gentile, 2003; Ko, Yen, Chen,
Chen, & Yen, 2005; Ng &Wiemer-Hastings, 2005; Soper& Miller, 1983; Wan &Chiou, 2006).
Other terms for the description of excessive or problematic gaming involve ‘‘videogame
dependence’’ (Griffiths & Hunt, 1995, 1998), ‘‘problematic game playing’’ (Salguero& Moran,
2002; Seay& Kraut, 2007), and ‘‘pathological gaming’’ (Johansson &Gotestam, 2004; Keepers,
1990). Regard lessof the used terminology, researchers agree that computer and videogame
overuse can cause a behavioural addiction (Griffiths, 2005). Addictive behaviour refers to
behaviour that is compulsive, excessive, psychologically or physically destructive, and
uncontrollable (Mendel son& Mello, 1986).
2.3. GUILT EMOTIONS
According to Lewis (1971), shame involves a negative evaluation of the self and guilt involves a
negative evaluation of a specific behaviour or action. Tangney (1991) has concluded that shame
and guilt are associated with different feelings such as: cognitions, public avoidance and
aggressive responses. Different researchers examined guilt and shame feelings in situations,
which defined as public–private distinctions (Wolf et al., 2010). Roos et al. (2014) have
concluded that guilt and shame could influence an individual’s social behaviours. Therefore, it
would be very beneficial to explore the feeling of guilty that developers can feel towards game
development and its negative damaging effect of addiction harming personal lives.
3. SENTIMENT ANALYSIS
Marketing managers comprehend the importance of customer sentiments in different services or
products through understanding the sentiment of internet users. Additionally, universities can take
advantage of sentiment analysis to recognise the opinion of students. Vocabulary based approach
can be used with conjunction of sentiment analysis; this is exercised for semantic annotation and
word suggestion. Machine learning strategies can classify sentiments into either positive or
negative.
Sentiment analysis tools have been used on non-technical domains including social media and
marketing. Rajat and Gaonkar (2017) have performed Sentiment Analysis on customers in order

49
to develop a rating forecasting tool, Lei and Xueming (2015) have measured the reputation of the
company based upon sentiment analysis of online customer. Das et al. (2017) conducted
sentiment analysis on airline industries; they classified sentiment of Twitter using Naïve Bayes
NB machine learning algorithm, and Rapid Miner RM. Furthermore, Support Vector Machine
SVM is one of the best performing machine learning technique nevertheless, NB can perform
better in a few cases.
Collomb et al. (2014) divided machine learning approach into the following four types. Machine
learning, which uses a learning algorithm via training data classification .Lexicon-based approach
involves calculation, using the semantic orientation of words or sentences in a document.Rule-
based approach classifies words in relevance to the number of positive and negative words. And
Senti4SD that is a classifier trained to support sentiment analysis in developers’ communication
channels (Calefato, et al., 2018). Senti4SD uses lexicon, keyword-based and semantic features.
In the proceeding sections, the modelling of sentiment analysis is exemplified and its three phased
stages are explained to end up with the experimental results that has been concluded from the data
collection.
3.1. MODELLING SENTIMENT ANALYSIS
Sentiment analysis guilt models composed of three stages: sentiment collector, processing and
classification as illustrated in Figure 1 below. Sentiments are collected from reddit.com. which is
an online forum for game developers, then a java tool is used to collect all sentiments while
removing html tags. Sentient collector has clustered 1000 sentiments and classified these
sentiments into two groups: guilt and no guilt.
After collecting the sentiment, a processing step is designed using Knime, which is a data
analytical and integration platform that includes components for machine learning, data mining
and web mining (Knime, 2018). Processing includes: punctuation eraser, case converter, stop
word removal and stemming. And the third step is the machine learning technique, where
sentiment analysis guilt model will use SVM, NV, and decision tree.
3.1.1 PRE-PROCESSING STEP
Pre-processing is the step of cleaning data and removing unwanted words. Pre-processing can
affect the accuracy of the sentiment analysis model (El-Masri, et al., 2017). Many pre-processing
techniques can be used on data such as: punctuation removal, remove stop words, repeated letters
and numbers and covert text to lower or upper case, stop word removal and stemming. Stop word
removal is the process of omitting connecting words like: and, the , to and so on, while stemming
is the process of returning a word to its basic form which is called stem. Porterstemmeris used in
the sentiment model, which is one of the known stemmers in text processing (Porter Stemmer,
2018).

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Figure 1.Sentiment Analysis Model
3.1.2. FEATURES SELECTION STEP
Features are inputs of the classifiers. They allow a more detailed analysis of the raw data. N-
grams are one of the most commonly used features (Arauj, et al., 2014). Bag of words (BOW) is a
representation method for object classification. BOW idea" is to quantize each extracted key point
into one of visual words, and then represent each image by a histogram of the visual words"
(Zhang et al., 2010). BOW is defined as a conjunction method with classification techniques.
Term Frequency Inverse Document Frequency (TF-IDF) is used to define what words in a
document is best used in a query. TF-IDF "calculates values for each word in a document through
an inverse proportion of the frequency of the word in a particular document to the percentage of
documents the word appears in" (Ramos, 2003). High TF-IDF numbers defines a strong
relationship with the document they appear in. Both BOW and TFIDF are used in Sentiment
analysis guilt model for selection of features.
3.1.3 MACHINE LEARNING STEP
Different machine learning approaches are used in sentiment analysis such as Naive Bayes (Go, et
al., 2009; Pang and Lee, 2004) and Support Vector Machines (Duwairi and El-Orfali, 2014).
Using the classifiers depends on the domain it is used in for example in education SVM
performed better than other classifiers (Altrabsheh et al., 2015). In the Sentiment analysis guilt
model classifiers used are: SVM, Naive Bayes and decision tree.
For the validation of classifiers, accuracy, precision and recall have been used. Classification
accuracy criteria focus"on how well the classifier assigns objects to their correct classes" (Hand,
2012). The precision is the correct predictions percentage and the recall is the instances percent
that were predicted as positive (Mostafa, 2009).
4. EXPERIMENTAL RESULTS
Sentiment analysis depends on the training data, the data was collected from Reddit.com forum
(Reddit, 2018). Game developers use Reddit forum for discussing any problem or topics related
to game development. Thousands sentiments were collected;five hundredslabeled guilt emotion
and another five hundredslabeled no guilt emotion. Sentiments were collected using a java
application that gets the text from the webpage, while removing the html tags. Classifiers were
then trained and tested in the thousands sentiments with the percent 80% for training and 20% for
testing as referred to Lewis and Catlett(1994).

51
Sentiment analysis guilt model tests the classifiers based on the 1000 sentiment and labelled these
sentiments into either guilt or not guilt. Naive Bayes, SVM and decision tree were used for the
classification of the sentiments. Sentiments collectors collect the sentiments and different
processing techniques were used to remove the noise from sentiment sentences. Table 2 shows
the results of the sentiment model. SVM was the highest accuracy level 77 however, this is
contradicting with Altrabsheh, et al. (2015) in which it not an educational field.
Table 2. Sentiment Analysis Guilt Model Results
5. CONCLUSIONS AND LIMITATIONS
Digital addiction affects the social life of a person, it is so important to understand the emotions
of the game developers who provide products that affect other person's life. In accordance to
previous studies, the guilt model was not tested on game developers. Sentiment analysis guilt
model can be used to test the guilt motion on the sentiments of game developers and can help in
solving guilt consequences emotions for game developers for the reason that guilt emotions can
develop to depression level in some cases.
The applied sentiment analysis guilt model in this research paper has the following limitations.
Reddit forum is the only one used to extract sentiments. Only three machine learning classifiers
were used: SVM, NV and decision tree nonetheless, other classifiers can be used such as Nearest
Neighbour. In addition, the data collection sentiment set is limited to 1000 sentiments. Feature
selection method was dependent on TF-IDF and BOW, further features selection can be expanded
to hold other semantic relations from Word Net tool as an example. Finally yet importantly,
sentiment analysis guilt model can be applied on different people category in other working fields
likethe educational field for instance.
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