228
HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION
OF LOVE: A COMPREHENSIVE
FRAMEWORK
Recibido: 10/10/2023 – Aceptado: 13/11/2023
Hernan Isaac Ocana Flores
Investigator - The University of Queensland
Brisbane – Australia
BACHELOR OF INFORMATION TECHNOLOGY WITH A MAJOR IN USER
EXPERIENCE DESIGN
hi.ocana@uqconnect.edu.au
https: 0000-0001-6258-3828
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into
the recognition of love: a comprehensive framework. Tierra Innita (9),
228-245.https://doi.org/10.32645/26028131.1254
229
HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION OF
LOVE: A COMPREHENSIVE FRAMEWORK
..............................................................................................................................................................................................................................................
Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Abstract
This framework considers complex emotions that can be analysed by various current technological
techniques, measures, and resources available nowadays. The author postulates an evolving
feedback mechanism between man’s psyche and technology development which denes man’s
psychological condition as well. The dimensions of the dynamic feedback loop comprise of
affective communication via technology, recognition of emotions through technology, and
moderation of emotions using technology. Thought the exploration of love other possibilities
for theoretical frames of system development, and systems’ needs for other emotions. Using
emotion recognition technology (e.g., facial expression analysis) and sentiment analysis,
these devices can recognise the users’ emotions, thereby making it possible for designers to
develop user-centred designs. What makes emotional technologies exist is the requirement of
combining the technologies which are highly precise and can understand the complicated traits
of life. Finally, the importance of emotional technologies advancements is discussed as t is now
essential to unify these technologies to reach unprecedented accuracies and studying the highest
complexity of life phenomena.
Keywords: Human Computer Interaction, Emotional technology, Dynamic Feedback Loop,
Emotion Regulation, Articial Intelligence,
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Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Introductory Framework
Emotion Generation and Expression
The convergence of technology and emotion today open new horizons for studying society. With
life revolving around platforms and devices, humans need to appreciate and understand how
technology can aid in understanding the emotional spectrum of their governing feelings. The
present research leads towards an assessment on how technology changed the process of
recognising feelings with the last decade of technologies. This study will provide an insight
into emotional issues in HCI (Human Computer Interaction) and UCD (User Centre Design)
considering that the scope of human feelings ranges from very positive emotions to sometimes
extremely negative ones.
Furthermore, it points out how technology was melded with aspects of feelings that
started when we as humans incorporated machines in our lives many years ago. Given the
strides made in intelligence as well as its ineffable bond with Big Data—a veritable resource in
shaping a Meta World – this research will explore three thematic areas vital to this study.
Dening Love in the Digital Age
Firstly, it’s needed to dene what love means in this context. Certainly, the most difcult
arguably, emotion that the human may experience would come down to only a few ones but
with no doubt near to love, such as there could be created a construction on the basis of truly
complicated emotion love, which may serve for a purpose, identication, and measurable.
This forms the foundation for a conceptual model for users to incorporate their feelings
into the system (Chan & Lievens 2019) According to Fisher et al. (2010), love is complicated
in itself involving feelings such as attachment, lust and attraction in regards to romance. With
this, we will explore various interventions that technology has developed for recognising the
different features of complexity.
Theories of Love
According to Sternberg’s Triangular Theory of Love, love is composed of three elements:
intimacy, passion, and commitment. Combinations of these elements determine various kinds
of love (Buolamwini & Gebru, 2018). Attachment theory argues that our relationship with
caregivers when young determine how we do love and relate to others later in our lives (Cambria
& Hussain, 2015). This leads to conclude that these basic elements of love can also be used in
other areas as metrics, including the eld of articial intelligence. These ndings congregated
with the proper equipment can aid in designing new systems that will help bridge the gap
between humans and machines (Buolamwini & Gebru, 2018; Cambria & Hussain, 2015).
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HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION OF
LOVE: A COMPREHENSIVE FRAMEWORK
..............................................................................................................................................................................................................................................
Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Components of a Complex Emotion, Love:
Desire for Proximity: The desire to be physically close or even emotionally connected to the
one who has been loved leads to satisfaction, assurance and contentment (Aron et al, 2005).
Commitment: It may involve also caring for, concern or passion for the welfare and
joyfulness of the person concerned even without present emotions (Bazarova & Choi, 2014 ) .
Altruism: Acts of selessness and sacrice associated with love are common features
that occur in most relationships among individuals (Batson et al, 1991) .
Passion: Passion is crucial in romance as it means strong feelings of romantic attachment,
ardent feelings of sexual interest and the yearning towards one another individual (Broekens,
et al, 2011) .
Emotional Attachment: Love is dened as an emotional attachment that involves one’s
feeling of love, liking and warmness for what he loves (Aron et al, 2005; Buolamwini & Gebru,
2018; Cambria & Hussain, 2015; Chan & Lievens 2019).
Neuroscience of Love:
Brain Chemistry: Research has shown that love is associated with the release of neurotransmitters
like dopamine, oxytocin, and serotonin, which play pivotal roles in feelings of pleasure, bonding,
and emotional attachment (Fisher et al, 2002) While Brain Imaging: Studies using fMRI have
shown that there are parts of the brain responsible for romantic feelings, especially the VTA and
caudate nucleus (Aron et al, 2005). This is shown and captured through visual signs and cues
projected on different measurable parameters using technological devices such as heart rate
monitors, eye tracking and pupil dilation.
The Role of Articial Intelligence (AI)
Nowadays, social media is an invaluable asset for data in regard to human relations. According
to Tika et al. (2021), the analysis of user-generated content and network connection on sites such
as Facebook and Twitter may reveal how people show and acknowledge their affection towards
each other. Big data made it easy to nd patterns, trends and possible directions, allowing
neural networks to learn and reinterpret data disruptively such as revolutionising the current job
market of copywriting, art, and even visual media.
For instance, Articial Intelligence has recently covered a wide spectrum as to include
areas of speech as well as picture recognition. According to Nijholt et al. (2018), a good AI
algorithm can be able to detect signs of emotion found in text messages or audio and as such
be capable to nd expressions of love (Aron et al, 2005; Pennebaker et al, 2011), through
digital means. Thus, complexities around culture, personal and situation perspectives that
make recognition of complex emotions more challenging. Is not enough to just use empathic
simulations AI for its support on the matching algorithms actually presents in some in online
dating systems (Fiore et al, 2008; Barrett et al, 2011).
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Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Emotional Analysis through technology
Micro-expression and response have also been measured by facial recognition technology
(Picard 1997), as such technology, utilises people’s facial cues that can be analysed to determine
whether they are expressions of affection or love (Buolamwini & Gebru, 2018; Fiore et al,
2008; Fisher et al, 2002).
On a similar manner, wearable devices like smartwatches and tness trackers were
also used to measure physiological responses to love which includes heart rate variability and
skin conductance, approach supported by Gaggioli et al. (2017). And recently, the launch of
Apple new VR has given people the opportunity to feel emotions when interacting with virtual
reality technology. The sense of immersion has been used to re-create scenarios of romance
and understand emotional reactions through facial capture, discussed by Riva et al., (2019) and
Rizzo et al. (2010).
Finally, the use of mobile phones like smartphones and wearable technology is one
of the most powerful approaches to monitoring and learning about human emotions (Ly,
2014). Ubiquitous, portable and sensor-rich, this enables real-time data collection relevant to
emotional states. A number of other physiological indexes such as heart rate variability, skin
conductance, and even facial expression can be tracked by mobile apps and sensors (D’Mello
et al, 2012). By using these data sources and machine learning algorithms, it is possible to
determine emotional states accurately.
It is also implied that a mobile phone will act as the core connection or “the heart”of the
system model and the mental model (Ly, 2014). The Ecological momentary assessment (EMA)
establishes that the mobile devices can provide an opportunity for individuals’ report their actual
emotional experiences at various moments while in the daily life (Barrett et al, 2011; Broekens,
et al, 2011; Park et al, 2016). This sustains and develops personalised interventions, and
emotional wellbeing apps tailored towards the emotional reading of the individual (Broekens,
et al, 2011; Dworkin et al, 2018).
Applications and Categorisation of Use Cases for Complex Emotion Detection in
Technology
Sentiment Analysis and Enhancing Emotional Well-being
Virtual assistants have evolved to recognise and respond to users’ emotional states, as such
companies employ sentiment analysis tools to gauge customer emotions and satisfaction.
Chatbots and customer service systems use this data to provide personalised responses, resolve
issues more effectively, and improve customer experiences (Broekens, et al, 2011)
For instance, they can detect stress or sadness in a users voice tone and provide
empathetic responses or suggest relaxation techniques (D’Mello et al, 2012; Norman, 2004).
In customer service, chatbots equipped with complex emotion detection capabilities have
become increasingly prevalent. These chatbots can analyse text-based interactions, such as chat
messages or emails, to gauge the emotional state of customers (Dworkin et al, 2018). When a
customer expresses frustration or dissatisfaction, the chatbot can adapt its responses to be more
233
HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION OF
LOVE: A COMPREHENSIVE FRAMEWORK
..............................................................................................................................................................................................................................................
Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
empathetic and understanding, enhancing the overall customer experience (De Choudhury et
al, 2016). This not only improves customer satisfaction but also demonstrates a commitment to
customer well-being.
For example, in the eld of mental health the technology’s ability to detect complex
emotions had enable better care. Mobile applications and platforms that employ emotion
recognition technology can monitor users’ emotional states over time (D’Mello et al, 2012;
Ly, 2014). This is done when signs of emotional distress, such as anxiety or depression, are
detected, these systems can provide personalized mental health resources, recommend therapy
sessions, or even connect users with mental health professionals (Dworkin et al, 2018).
Online Dating Platforms Matching Based on Emotional Compatibility
Online dating platforms like eHarmony and OkCupid now utilize advanced algorithms to assess
emotional compatibility between potential partners (Verduyn et al, 2017). These platforms
analyse users’ online behaviour, communication patterns, and expressed emotions to facilitate
more meaningful and successful romantic connections (Eastwick & Finkel, 2008; Fiore et al,
2008; Verduyn et al, 2017).
Emotionally-Informed Prole Matching
Modern dating platforms employ sophisticated algorithms that delve into users’ online behaviour
and communication patterns to evaluate emotional compatibility (Cooper et al, 2007; Fiore et
al, 2008). These algorithms analyse factors such as the sentiment of messages exchanged, the
tone of user proles, and even the emotional content of photos shared. This allows for a more
in-depth assessment of a users emotional disposition and preferences.
Predictive Analytics for Relationship Success
Emotional compatibility analysis is used together with predictive analytic so that dating sites
can be improved and make a prot due to more customers. Through matching people with
complementary emotions, these sites increase chances of success at long-term relationships
(Batson et al, 1991; Fiore et al, 2008). This makes users more willing to form relationships with
people whose emotions they understand and communicate well with other people.
Improved User Experience
Matching on the basis of emotional analyses increases possibility of matches which are
mutually compatible and makes the users have a positive experience. This in turn increases the
chances of nding a more suitable match, creating and satisfying the user relations within the
system. As a result, it enhances user’s loyalty as well as provide a platform for mouth to mouth
advertisement for the given platform (D’Mello et al, 2012).
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Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Emotionally Intelligent Technologies
Emotion recognition has evolved as a critical aspect of educational technologies and e-learning
platforms in measuring student’s emotions. For example, if a student shows stress when
trying to solve a math problem, the system can offer more assistance or alternative learning
objects (De Choudhury et al, 2016). Emotion recognition is also included as part of software
development for gamers to make it more interesting. This allows games to alter their level of
difculty depending on how an individual is responding emotionally, making the gameplay
more interactive (D’Mello et al, 2012; Kivikangas et al, 2011; Riva et al, 2019).
Tailored Recommendations
The platform becomes better at guring out what really attracts users emotionally, which
improves the quality of match suggestions that extend beyond supercial dimensions like
lifestyle preferences, interests and likes (Park et al, 2016). This leads to better customized
matchmaking resulting to high probability for true relations.
Complex Emotion Regulation and Feedback Loops For Users and Systems
Emotion Regulation Strategies
Emotions form a crucial element of human existence which inuence the way people see, think
or relate with others. In today’s age of digital revolution, when gadgets have crept into every
corner of the human lives, its often that individuals must cope with emotions within this context.
These processes involve emotion regulation strategies (both conscious and unconscious), which
include how we manage the digital world, choose things, and keep a balance for our mental
health. This study examines a variety of emotions regulation strategies and what leads to an
experience when using technological tools or equipment.
Suppression of Negative Emotions
The suppression of negative emotional expressions when using various technological devices
is one widely known emotion regulation tactic. People usually try to hide such emotions as
boredom, frustration, irritation, or disappointment in order not to spoil the web experience
for other members (Broekens, et al, 2011; De Choudhury et al, 2016). With this approach, it
is possible to maintain high-quality digital communications while avoiding conicts in online
interactions.
Emotion Recognition Tools
Recently, emotion recognition has been introduced as a potent means of helping persons take
control of and determine how they feel. Virtual assistants, which include devices embedded
with sentiment analysis facilities, have the ability to infer emotion in a spoken utterance or
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HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION OF
LOVE: A COMPREHENSIVE FRAMEWORK
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Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
written text, and respond appropriately (De Choudhury et al, 2016; Pennebaker et al, 2011;
Rosenfeld & Thomas 2012). For example, when a user appears to be sad, the tool may comment
that they are feeling sorry for them and recommend showing funny material in order to elevate
their spirits.
Social Media Self-Presentation
On social media platforms, individually, they portray their ideal self-online through selective
sharing of pleasing experiences and feelings and not the unpleasant ones (Bazarova & Choi,
2014; Cowley et al, 2008). This selective self-presentation also affects other users, and may
have an impact on their emotional state (Shiffman et al, 2008).
Digital Detox and Unplugging
Recognising the potential for technology-induced stress and emotional exhaustion, people opt
for unplugging and abstinence techniques. This means that individuals intentionally restrict
and scan time in order to take regular pauses from online gadgets, and detach themselves of the
digital world by interacting with nature or doing something that is not connected to the online
world (Bazarova & Choi, 2014 ). The rst is a restorative approach directed toward bringing the
emotional balance back within an ever-increasingly digital environment.
Online Self-Disclosure
The act of sharing personal experiences and emotions online serves as a coping mechanism for
many. People share their joys and sorrows, seeking social support and validation (Gaggioli et
al, 2017) from [online] communities (Bowlby, 1969; Toma et al, 2008). This form of emotional
expression could assist people in making sense of their feelings as well as help them get
sympathy and advice from other people (Cowley et al, 2008; Toma et al, 2008).
Mindfulness and Meditation Apps
Mindfulness and Meditation apps are an avenue through which people can regulate their
emotions (Cowley et al, 2008; De Choudhury et al, 2016). The rst category of these apps
directs users on the way of self-reective and emotional regulation exercises (Hitsch et al,
2010). Individuals become emotionally resilient if they introduce mindfulness in their daily
activities and minimize technological stress (Rosenfeld & Thomas 2012; Shiffman et al, 2008).
Online Information Seeking
For instance, people seek help from the worldwide net when they experience an emotional
challenge or uncertainty (Buolamwini & Gebru, 2018; Subramani et al, 2017). It enables users
understanding of their situations and affecting either reinforcing the feelings or inuencing
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Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
these as it provides users with possible resolutions against some of the emotional challenges
that emanate from it.
Feedback and Reection
Other users willingly request and provide feedback and analyse how is the usage of particular
technologies. These can range from providing a review to apply for such things as tracking
apps that show screen time, or use journaling that tracks what they experience at a certain point
(Consolvo et al, 2008; Hitsch et al, 2010; Subramani et al, 2017). Therefore, they are able to
learn what helps and what does not help them attain their emotional goals to enable them to
monitor their technology habits.
Personalization and Customization
This is one instance of technology devices that foster the creation of personalised digital
environments by users’ emotional preferences (Chan & Lievens 2019; Fiore et al, 2008;
Hitsch et al, 2010; Subramani et al, 2017), including personalised playlists or customisable
user-interfacing. Such feeling of control provides users a more emotionally rewarding digital
experience.
Empathy and Emotional Support Features
The design of empathetic technologies using chatbots or virtual friends may be used for
emotional support and companionship (Hitsch et al, 2010; Kim & Dey, 2015; Primack et al,
2017). This is as these technologies provide a secure platform through which users can articulate
and control their emotions.
The complexities involved with adapting strategies in relation to technological utilisation
reect ever-changing human interactions with their digital equipment. Either conscious,
subconscious, such skills help navigating through the digital age and seeking good health while
striving for well-being and a positive digital experience.
Dynamic Feedback Loop
The Interplay Between Technology and Emotion
The relationship between technology and emotion is not static; instead forms a dynamic
feedback loop where technology devices can both affect and be inuenced by human emotions
(Aron et al, 2005; Cambria & Hussain, 2015; Cooper et al, 2007; Fiore et al, 2008; Hitsch
et al, 2010; Kim & Dey, 2015). This complex relationship between technology and emotion
includes the process of generating, recognising, and regulating emotions. This study reveals
these diverse perspectives of this circular process, explaining how it relates to our moods and
the development of technological advances.
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HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION OF
LOVE: A COMPREHENSIVE FRAMEWORK
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Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Emotion Generation by Technology
The use of different types of technology devices can elicit multiple feelings among users (Aron et
al, 2005). For instance, video games are meant to produce excitement, challenge, and happiness
among other feelings (Park et al, 2016). The nature of content on the platform, and also users’
interactions on social media platforms can make individual feel happy, angry or envious (Park
et al, 2016). Stress or anxiety monitoring wearable device that can give people an insight into
how they feel at any given moment (Barrett et al, 2011; Nijholt et al, 2018).
Emotion Recognition by Technology
Technology devices are programmed with abilities to identify and react to human emotions using
advancements in AI and machine learning. The emotion recognition technology is capable of
analysing facial expressions, voice tone as well text sentiment to derive users’ emotional state
(Kivikangas et al, 2011; Liu, 2012). Sentiment analysis is an important tool for virtual assistants,
chat bots, and customer service systems who gauge user emotions and craft personalised replies
(Liu, 2012; Park et al, 2016). These capabilities enable technology to provide empathetic and
personalised interactions.
Emotion Regulation Through Technology
Technology also serves as a tool for emotion regulation. Mobile apps offer guided meditation
sessions and stress-relief exercises, assisting users in managing their emotional well-being
(Broekens, et al, 2011; D’Mello et al, 2012). In addition, technology also functions as an
auxiliary measure of regulating emotions. The mobile apps include guided meditation sessions
and stress relief exercises that help manage individuals’ emotions. VR has also been used
in treating of some conditions e.g. PTSD by introducing the users to virtual but controlled
emotional stimuli (Buolamwini & Gebru, 2018; Kivikangas et al, 2020; Liu, 2012). Moreover,
some biofeedback wearable technologies will enable the user’s emotion regulation via current
physiological information (Cambria & Hussain, 2015).
User Feedback Informing Device Design
This key component of the dynamic feedback loop is user feedback because it gives out ideas
on how users feel about the various technologies they use. Furthermore, users can give feedback
back that is either explicit through reviews or user surveys or it may be implicit use patterns and
their interaction with the device. Developers will have an opportunity to learn their emotions
about technology, iterating new options in order to improve the user emotional experience
(Chan & Lievens 2019; Kivikangas et al, 2020).
Iterative Design and Emotional Design Principles
Emotional design takes place through user feedback and then the iterative design. The concept
of emotional design envisages ways in which technology could evoke certain feelings for
purpose of creating more attractive and user-friendly experiences (Chan & Lievens 2019). For
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Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
example, the layout of a mobile application can focus on reducing frustrations while increasing
the joy stimuli from users (Norman, 2004).
Impact on Mental Health and Well-being
Mental health and general well-being hinge on the quality of the dynamic feedback loop between
technology and emotion. Such positive interactions can lead to better mental health outcomes
(Cooper et al, 2007; Primack et al, 2017). On the other hand, negative emotional experiences
associated with technology use can be detrimental to well-being (Primack et al, 2017).
Ethical Considerations and User Consent:
Along with each move becomes more attuned technology to user emotions, a lot of ethical matters
arise. Agency regarding users’ sharing and use of emotional data (Consolvo et al, 2008). The
dynamic relationship between technology and the emotions must include transparency, informed
consent and strong data privacy measures that protect self –determination and wellness or the
individual.
In the midst of these technology advancements, however, the consideration of ethics
abounds. Love recognition technology entails issues on ethics of privacy and consent. According
to Bahameish (et al., 2019), any development must be balanced with ethics (Dworkin et al,
2018; Moor et al, 2019). This, however, explains why there is a fundamental necessity for
ethical standards in healthcare management.
Despite the potential benets for inclusion of technology in love, there are some ethical
considerations that can’t be ignored such as privacy and permission. As Moor et al. (2019)
emphasise, striking a balance between technological advancement and ethical responsibility
is indispensable for the user interaction. The need for privacy is a concern that comes with the
emotion-sensing devices and systems which have ethical considerations since it collects private
information and raises queries on its permission on data protection (Park et al, 2016). Lastly,
automation overload presents an ethical challenge due to the possibility of becoming emotionally
disconnected from genuine human empathy and feelings (Bazarova & Choi, 2014). Hence,
although technology provides useful knowledge about emotional recognition, it should be used
with care and combined with human understanding, in order to understand fully the complexity
of all types of emotions.
Discussion and Conclusion
The complex interweaving of emotions and technology has revealed various opportunities and
obstacles, which lie on this new territory. The discussion focuses more on the intricacies of this
relationship discussing the main issues discussed earlier in our study. Nevertheless, while this
technology proposition is very powerful for identifying complexities of the human life, it comes
with its own challenges and ethics. The dependence of technology in interpreting emotions has
raised several issues including issues on privacy, consent and securing of data, which in turn
raises issues on bias, fairness, and inclusiveness in emotion recognition algorithm.
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HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION OF
LOVE: A COMPREHENSIVE FRAMEWORK
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Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Therefore, in moving along with this complicated dilemma of the ever-changing
relationship between technology and emotions it is important to be careful and have strong
moral values so that the innovations could really have positive effects on our understanding of
the psychological well-being of humans. To reiterate the proposed process involves a generation,
recognition, and regulation of emotion from users’ perspective which affects the way devices
are designed and interactions. Leaving the research community to once again dwell on this
continuously interplay that evolves and raises ethical questions on what matters most; whether
the technology improves or worsens people’s emotional lives in modern times.
Emotion Recognition and Its Limitations:
Technology’s capability to identify and react to people’s emotions has led to the emergence
of novel forms of communication between computers and humans. The advent of emotion
recognition algorithms, powered by machine learning and articial intelligence advances,
enables devices to identify not just simple feelings such as joy or sorrow but also subtle states
like aggravation and bafement. This ability has thus laid grounds for emotionally intelligent
virtual assistants, emotionally enhanced gaming experiences and mental tracking devices.
Nevertheless, one important point that must be addressed in this regard is the weaknesses
of the existing emotive identication engineering. Despite being relying on visible cues like
facial expression or voice tone that sometimes do not adequately reect the intricacy of human
feelings, these systems can be used in numerous applications be successful. However, there is
a possibility of algorithmic bias where some groups may receive wrong classication or under
representation. Thus, with the advancement of technology going forward, the importance of
taking care that these shortcomings so as to achieve fairness and accuracy in the facial emotion
recognition spectrum.
Ethical Considerations:
Considerations related to ethics surround emotions and technology. There are privacy concerns
surrounding collecting and analysing emotional data considering the condential nature of such
information regarding whether an individual has given permission or not, and safe guarding the
data (Norman, 2004). Protecting users’ rights demands transparent and ethical data practices
like informed consent and strong data safeguards (Verduyn et al, 2017).
Furthermore, there is a need for careful examination of the possibility of emotional
abuse associated with technology. User emotions may even be inuenced by the design of
user interfaces and content algorithms in accidental and harmful ways (Buolamwini & Gebru,
2018; Saeb et al, 2015). However, designers should be vigilant about creating a healthy balance
between keeping users engaged as wellbeing emotionally sound.
Emotional Well-being and Mental Health:
The effects of technology on emotional well-being and mental health have become a rising area
of concern as well as study. Technology has provided tools like meditation apps that help to
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Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
manage and regulate emotions; however, screen time and frequent usage of social media can
cause depression and loneliness (Primack et al, 2017; Riva et al, 2019; Saeb et al, 2015). The
need for society, as well as people individually, to balance the gains of technology against its
possible hazards has become a contemporary challenge.
Emotional Design and User Experience:
The effects of technology on the feel can ease of use as well as the perceived notion of the user
towards the mental model of the application will inuence mood. Just by the starting of the
appear and visual cues as well as the auditory and physical ones the user has been prompted
with a base of emotion experience, such in a similar manner can be served to create a feedback
loop back for emotion measurement.
Emotion Regulation Strategies:
The use of emotion regulation strategies has now become a critical aspect with regards to
interactions in the digital environment. People use various strategies like selective self-
presentation, mindfulness aided through apps and online searching of information for their
digital individual emotional feelings (Tika et al, 2021). These strategies shed light on how user
adapts themselves for the same emotional challenges posed by the technological development.
Future Horizons:
The future for possibility becomes an ever-widening vista as technology progresses with regard
to emotion and technology. As virtual reality experiences become increasingly immersive and
emotional, they are likely to be applied for use in both therapy and education. As this line
continues to blend, emotionally intelligent robots are being designed for companionship and
support. The conjunction of emotion and technology is an ever-expanding arena for research,
yet a constant self-examination and moral assessment is essential.
While the premise of this research is that technology can recognize complex emotions
digitally, this poses numerous difculties. Pennebaker (2011) explains this complexity of
affective computing, stating that accurate detection of love in digital interaction should consider
subtlety and detailed programming. Such an argument shows how hard it is to use technology
to recognise love. However, the understanding of this issue is extremely complicated, and there
has to exist an alternative view of perception in HCI so that instead of the emotional reader of
the system being solely dependent on the combination of all aforementioned systems, the subtly
combination of both.
For example, wearable devices, such as smartwatches and tness trackers, have
expanded our ability to monitor physiological responses associated with love. Research by
Bahamesh (2019) show that these factors include heart rate variability and skin conductance,
which are indicators for studying emotions in intimate love relations (Moor et al, 2019). Facial
recognition technology provides one distinctive way of identifying love on the face, As Gaggioli
241
HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION OF
LOVE: A COMPREHENSIVE FRAMEWORK
..............................................................................................................................................................................................................................................
Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
et al. (2017) suggest, examinations of micro-expressions and physiological reactions allow for
the identication and exploration of someone’s affections. Riva et al. (2019) demonstrate that
emotions arising from romantic scenes in a simulated environment are authentic and this can
foster love research in a controlled setting (Rizzo et al, 2010). Additionally, the analysis of
user-generated content and network connections on platforms such as Facebook and Twitter has
become important sources of data for understanding love expression (Tika, 2021).
To produce it will take the unity of all the systems or the “Internet of everything” (IoE)
in the full realization of power of all its segments with which it is possible to trace better
the intricacy of mankind and human understanding for centuries until now by computational
potential (Saeb et al, 2015). This diversity and combination of approaches is its the strength for
accuracy: Technology today contributes to improving emotional life and is very progressive
in providing an opportunity to nd suitable partners for relationship according to emotional
compatibility. Providing the developments and current state of the technology this framework
provided also some new ethical issues implying that it is necessary strike such a balance with
both progress in technology and man’s principles.
Recognising the uidity aspect in the domain of emotion and technology. Our exploration
process helps us in forming bases of future research, innovations, and ethic concerns on that
basis. As a result, the future development of technology devices will be characterised by
continual advancements in emotion regulation strategies, emotional designer principles, and
the inclusion of user feedback, to ensure that these devices remain in touch with our emotions.
Therefore, it would be possible to understand complex emotions protecting our mental
health, while technology develops its boundaries in the eld of emotion and technology. With
virtual realism, the users would want to experience new forms of immersion that can be applied
in both education and practice. The boundary with the automatization is becoming more vague
as emotionally intelligent robots are being constructed for companionship and help. This area of
a dynamic industry where the power of emotion fuses with technological capability possesses
innite possibilities. However, it is imperative for continuous deliberation and ethics.
Finally, this interaction between emotionality and technology is a complicated domain
that is woven into the weft of contemporary society. This has led the present framework show
the promise and pitfalls of this association, emphasising the need for responsible innovation
and ethical questions that must be considered. This shows this complex area, where we have
to remember that technological factors greatly inuence our emotional experience and life in
general trying to maintain balance but possible coexistence within disruptive Digital era.
References
Aron, A., Fisher, H. E., Mashek, D. J., Strong, G., Li, H., & Brown, L. L. (2005). Reward,
motivation, and emotion systems associated with early-stage intense romantic love.
Journal of Neurophysiology, 94(1), 327-337. Retrieved from: https://pubmed.ncbi.nlm.
nih.gov/15928068/
242
..............................................................................................................................................................................................................................................
Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Barrett, L. F., Mesquita, B., & Gendron, M. (2011). Context in emotion perception. Current
Directions in Psychological Science, 20(5), 286-290. Retrieved from: https://journals.
sagepub.com/doi/10.1177/0963721411422522
Batson, C. D., & Shaw, L. L. (1991). Evidence for altruism: Toward a pluralism of prosocial
motives. Psychological Inquiry, 2(2), 107-122. Retrieved from: https://psycnet.apa.org/
record/1992-01389-001
Bazarova, N. N., & Choi, Y. H. (2014). Self-disclosure in social media: Extending the functional
approach to disclosure motivations and characteristics on social network sites. Journal
of Communication, 64(4), 635-657. Retrieved from: https://www.researchgate.net/
publication/264792898_Self-Disclosure_in_Social_Media_Extending_the_Functional_
Approach_to_Disclosure_Motivations_and_Characteristics_on_Social_Network_
SitesAn_earlier
Bowlby, J. (1969). Attachment and loss: Vol. 1. Attachment. Basic Books.
Broekens, J., Brinkman, W. P., & Heylen, D. (2011). The impact of perceiving affective body
language and facial expressions on the perception of emotions in virtual characters.
International Journal of Human-Computer Studies, 69(11), 870-878.
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in
commercial gender classication. In Proceedings of the 1st Conference on Fairness,
Accountability and Transparency (pp. 77-91). Retrieved from: https://proceedings.mlr.
press/v81/buolamwini18a.html
Cambria, E., & Hussain, A. (2015). Sentic computing: A common-sense-based framework
for concept-level sentiment analysis. IEEE Transactions on Cybernetics, 45(3), 612-
626. Retrieved from: https://www.researchgate.net/publication/280734667_Sentic_
Computing_A_Common-Sense-Based_Framework_for_Concept-Level_Sentiment_
Analysis
Chan, A. D. C., & Lievens, F. (2019). Wearable Technology and Embodied Accounts: An
Integrative Review. Organizational Research Methods, 22(2), 475-509.
Subramani P., Chuon, Y. H., Lee, Y. R., & Aaseer, Y. S. (2017). Smartphone usage and increased
risk of mobile phone addiction: A concurrent study. International Journal of Psychiatry
in Clinical Practice, 21(3), 183-188. Retrieved from: https://www.ncbi.nlm.nih.gov/
pmc/articles/PMC5680647/
Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y., Froehlich, J., Harrison, B., ... &
Klasnja, P. (2008). Activity sensing in the wild: A eld trial of ubit garden. In
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp.
1797-1806). Retrieved from: https://www.researchgate.net/publication/221518229_
Activity_Sensing_in_the_Wild_A_Field_Trial_of_UbiFit_Garden
Cooper, A., Reimann, R., & Cronin, D. (2007). About face 3: The essentials of interaction
design. John Wiley & Sons. Retrieved from: https://books.google.com.ec/
books?id=e75G0xIJju8C&printsec=frontcover&source=gbs_ge_summary_r&cad=0
243
HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION OF
LOVE: A COMPREHENSIVE FRAMEWORK
..............................................................................................................................................................................................................................................
Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Cowley, B., Charles, D., Black, M., & Hickey, R. (2008). Toward an emotion sensitive world.
IEEE Transactions on Computational Intelligence and AI in Games, 1(4), 74-87.
De Choudhury, M., Kıcıman, E., Dredze, M., Coppersmith, G., & Kumar, M. (2016, February).
Discovering shifts to suicidal ideation from mental health content in social media. In
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp.
2098-2110).
D’Mello, S. K., & Graesser, A. C. (2012). AutoTutor and affective autotutor: Learning by
talking with cognitively and emotionally intelligent computers that talk back. ACM
Transactions on Interactive Intelligent Systems (TiiS), 2(4), 23.
Dworkin, J. D., Connell, I. D., & Doty, J. (2018). A typology of ethical issues in qualitative
research. Qualitative Psychology, 5(2), 165-180.
Eastwick, P. W., & Finkel, E. J. (2008). Speed-dating. Current Directions in Psychological
Science, 17(3), 193-197.
Fiore, A. T., Taylor, L. S., Mendelsohn, G. A., & Hearst, M. (2008). Assessing attractiveness in
online dating proles. In Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems (pp. 797-806).
Fisher, H. E., Aron, A., Mashek, D. J., Li, H., & Brown, L. L. (2002). Dening the brain systems
of lust, romantic attraction, and attachment. Archives of Sexual Behavior, 31(5), 413-
419.
Gaggioli, A., Pallavicini, F., Morganti, L., Serino, S., Scaratti, C., Briguglio, M., ... & Riva,
G. (2017). Experiential virtual scenarios with real-time monitoring (interreality) for the
management of psychological stress: A block randomized controlled trial. Journal of
Medical Internet Research, 19(3), e92.
Hitsch, G. J., Hortacsu, A., & Ariely, D. (2010). What makes you click?—Mate preferences in
online dating. Quantitative Marketing and Economics, 8(4), 393-427.
Kim, J., & Dey, A. K. (2015). Simultaneous modeling of multiple physiological signals for
emotion recognition. In Proceedings of the 2015 ACM International Joint Conference
on Pervasive and Ubiquitous Computing (pp. 699-710).
Kivikangas, Ekman, I., Järvelä, S., & Ravaja, N. (2020). Stimulus Games, retrieved from:
https://www.researchgate.net/publication/346387331_Stimulus_Games?_tp=eyJjb250
ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoicHJvZmlsZSJ9fQ#full-
text
Kivikangas, J. M., Chanel, G., Cowley, B., Ekman, I., Salminen, M., Järvelä, S., & Ravaja, N.
(2011). A review of the use of psychophysiological methods in game research. Journal
of Gaming & Virtual Worlds, 3(3), 181-199, American Psychological Association.
Retrieved from: https://doi.org/10.1386/jgvw.3.3.181_1
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human
Language Technologies, 5(1), 1-167. Retrieved from: https://doi.org/10.2200/
S00416ED1V01Y201204HLT016
244
..............................................................................................................................................................................................................................................
Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Ly, K. H., Trüschel, A., Jarl, L., Magnusson, S., Windahl, T., Johansson, R, & Andersson, G.
(2014). Behavioural activation versus mindfulness-based guided self-help treatment
administered through a smartphone application: A randomised controlled trial. BMJ
Open, 4(1), e003440. Retrieved from: https://bmjopen.bmj.com/content/4/1/e003440.
info
Moor, I., Taylor A., Grigor I., Bahameish, Putnam C., Hanschke C., Rana A.,(2019). Can
Changes in Heart Rate Variability Represented in Sound be Identied by Non-Medical
Experts - HasAnswers: Development of a Digital Tool to Support Young People to
Manage Independent Living - Efcacy of Film for Raising Awareness of Diverse Users.
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
(CHI ’19). Retrieved from: https://dl.acm.org/doi/proceedings/10.1145/3290607
Nijholt, A., Reidsma, D., & van Welbergen, H. (2018). Nonverbal and Bodily Interaction in
Ambient Entertainment. Japanese Journal of Applied PhysicsRetrieved from : https://
www.researchgate.net/publication/228364266_Nonverbal_and_Bodily_Interaction_in_
Ambient_Entertainment.
Norman, D. A. (2004). Emotional design: Why we love (or hate) everyday things. Basic books.
Park, N., Jin, B., & Jin, S. A. (2016). Effects of self-disclosure on relational intimacy in
Facebook. Computers in Human Behavior, 55, 399-406.
Pennebaker, J. W., & Chung, C. K. (2011). Expressive writing: Connections to physical and
psychological health. In The Oxford Handbook of Health Psychology (pp. 417-437).
Picard, R. W. (1997). Affective computing. MIT Media Lab Perceptual Computing
Section Technical Report. Retrieved from: https://www.scirp.org/reference/
ReferencesPapers?ReferenceID=1714599
Primack, B. A., Shensa, A., Sidani, J. E., Whaite, E. O., Lin, L. Y., Rosen, D., ... & Miller,
E. (2017). Social media use and perceived social isolation among young adults in the
US. PLOS ONE, 12(8), e0182146. DOI 10.1016/j.amepre.2017.01.010 Retrieved from:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5722463/
Riva, G., Banos, R. M., Botella, C., Wiederhold, B. K., & Gaggioli, A. (2019). Positive
technology and active ageing: The rehabilitation gamication example. Stud Health
Technol Inform, 267, 159-165. DOI:10.3233/978-1-61499-425-1-44 Retrieved from:
https://www.researchgate.net/publication/268923757_Positive_Technology_for_
Healthy_Living_and_Active_Ageing
Rizzo, A. S., Difede, J., Rothbaum, B. O., Reger, G., Spitalnick, J., Cukor, J., ... & Pair, J.
(2010). Development and early evaluation of the Virtual Iraq/Afghanistan exposure
therapy system for combat-related PTSD. Annals of the New York Academy of Sciences,
1208(1), 114-125. DOI 10.1111/j.1749-6632.2010.05755.x Retrieved from: https://
pubmed.ncbi.nlm.nih.gov/20955333/
245
HUMAN COMPUTER INTERACTION’S
INSIGHTS INTO THE RECOGNITION OF
LOVE: A COMPREHENSIVE FRAMEWORK
..............................................................................................................................................................................................................................................
Cómo citar este artículo:
Ocana, H., (Enero – Diciembre 2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra
Innita (9), 228-245.https://doi.org/10.32645/26028131.1254
Rosenfeld, M. J., & Thomas, R. J. (2012). Searching for a mate: The rise of the Internet as a
social intermediary. American Sociological Review, 77(4), 523-547. Retrieved from:
https://journals.sagepub.com/doi/full/10.1177/0003122412448050
Saeb, S., Zhang, M., Karr, C. J., Schueller, S. SM., Corden, M. E., Kording, K. P., & Mohr, D.
C. (2015). Mobile phone sensor correlates of depressive symptom severity in daily-life
behavior: an exploratory study. Journal of Medical Internet Research, 17(7), e175. DOI
10.2196/jmir.4273 Retrieved from: https://pubmed.ncbi.nlm.nih.gov/26180009/
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual
Review of Clinical Psychology, 4, 1-32. Retrieved from:https://www.annualreviews.
org/doi/abs/10.1146/annurev.clinpsy.3.022806.091415
Tika Adilah1, Supendar H., Ningsih R., Muryani S., and Solecha K., (2021). Sentiment Analysis
of Online Transportation Service using the Naïve Bayes Methods, Journal of Physics:
Conference Series. DOI 10.1088/1742-6596/1641/1/012093 Retrieved from: https://
iopscience.iop.org/article/10.1088/1742-6596/1641/1/012093
Toma, C. L., Hancock, J. T., & Ellison, N. B. (2008). Separating fact from ction: An examination
of deceptive self-presentation in online dating proles. Personality and Social Psychology
Bulletin, 34(8), 1023-1036. Retrieved from: https://doi.org/10.1177/0146167208318067
Verduyn, P., Ybarra, O., Résibois, M., Jonides, J., & Kross, E. (2017). Do social network sites
enhance or undermine subjective well-being? A critical review. Social Issues and Policy
Review, 11(1), 274-302. Retrieved from: https://doi.org/10.1111/sipr.12033