User Emotions Research Articles

Sustainable Emotional Design Based on Industry 4.0 for Industrial Nougat Packaging

Nowadays, digitalisation is present in all links of the product life cycle and within product design. Packaging is a critical element that affects the customer’s purchase intention. Therefore, packaging is one of the elements for which it is positive to include techniques and methods based on Industry 4.0 to obtain results from users and bring them closer to the process. The relationship between packaging and the expected quality and emotions of users is a topic that is addressed in the social axis of sustainability and involves an effort to strengthen the relationship with the user. This article studies the expected quality of nougat in terms of packaging shape and colour using ANOVA analysis. For this purpose, a survey was carried out among 122 participants in Spain and Mexico. The main conclusion is that packaging with a combination of three shapes (rectangle, square and triangle) and colours (yellow, orange and red) enhances consumers’ emotions of Admiration, Satisfaction, Pleasant Surprise, Attraction, Fascination and Joy.

Music Recommendation System based on Emotions in User’s Social Media behaviour

The modern lifestyle makes people more competitive. It can lead to more stressful situations in our lifestyle. With the changes in human emotional behaviour, they tend to share their feelings on social media platforms rather than communicating with relatives. Studies proved that people used to listen to music to avoid emotional situations in their life. But there is no proper way to get the most accurate music to listen to and avoid emotional conflicts. Resolving these conflicts, the music recommendation system based on emotion introduced. It analyses the users’ recent social media content and detects the various kind of emotions. The lyrics analysing was done using natural language processing techniques to identify the music emotions. Most people pay attention to the meaning of the songs, that was the major reason to consider the emotions of the lyrics. The research was considered on a language-independent platform for both English and Sinhala.

A pilot study of measuring emotional response and perception of LLM-generated questionnaire and human-generated questionnaires

The advent of ChatGPT has sparked a heated debate surrounding natural language processing technology and AI-powered chatbots, leading to extensive research and applications across various disciplines. This pilot study aims to investigate the impact of ChatGPT on users' experiences by administering two distinct questionnaires, one generated by humans and the other by ChatGPT, along with an Emotion Detecting Model. A total of 14 participants (7 female and 7 male) aged between 18 and 35 years were recruited, resulting in the collection of 8672 ChatGPT-associated data points and 8797 human-associated data points. Data analysis was conducted using Analysis of Variance (ANOVA). The results indicate that the utilization of ChatGPT enhances participants' happiness levels and reduces their sadness levels. While no significant gender influences were observed, variations were found about specific emotions. It is important to note that the limited sample size, narrow age range, and potential cultural impacts restrict the generalizability of the findings to a broader population. Future research directions should explore the impact of incorporating additional language models or chatbots on user emotions, particularly among specific age groups such as older individuals and teenagers. As one of the pioneering works evaluating the human perception of ChatGPT text and communication, it is noteworthy that ChatGPT received positive evaluations and demonstrated effectiveness in generating extensive questionnaires.

Conditioned Canine Cadavers for Near-Natural Interprofessional Veterinary and Human Surgery Training.

Both medical and veterinary students find that the use of cadavers is critical to learning anatomical structures and surgical techniques. The use of human cadavers and the resulting user emotions are driven by serious ethical issues that are currently much less pronounced in veterinary education. Ethically sourced canine cadavers, thus, are more readily available. Aesthetics such as odor and visual appearance, though, influence both learner and educator motivation.We have investigated a way of delaying cadaver decomposition by post-mortem in situ, chemical-free, gastrointestinal lavage.We are convinced that canine cadavers, conditioned as described here, will improve the outcome of cadaver-based surgical skills training by facilitating preparation, reducing the number of required cadavers, postponing decomposition, improving the surgeon's haptic-tactile response to organ and tissue handling and suturing, and, possibly most importantly, increasing learners' and educators' focus due to the significantly improved aesthetics. We hypothesize that skill transfer for medical students and doctors, because of the similar abdominal anatomy, may be easier when training with conditioned canine cadavers as compared to artificial simulators or pigs in vivo.

Eliciting and modeling emotional requirements: a systematic mapping review.

Considering users' emotions plays an extremely crucial role in the adoption and acceptance of recent technology by the end user. User emotions can also help to identify unknown requirements, saving resources that would otherwise be wasted if discovered later. However, eliciting and modeling users' emotional requirements in software engineering is still an open research area. This systematic mapping review analyzes emotional requirements (ER) practices in software engineering from two perspectives: elicitation and modeling. For elicitation techniques, we investigate the techniques, evaluation methods, limitations, and application domains. For modeling techniques, we examine the modeling languages, analyses, limitations, and domains. We systematically reviewed studies on emotional requirements engineering published between 1993-2023 and identified 46 relevant primary studies. A total of 34 studies investigated ER elicitation techniques, five examined modeling techniques, and seven covered both. Illustrative case studies were the main evaluation method for proposed elicitation techniques. Identified limitations include time consumption and extensive human involvement. The dominant application domains were healthcare and well-being, and game development. This review summarizes the current landscape of emotional requirements research, highlighting key elicitation and modeling techniques, evaluations, limitations, and domains. Further research can build on these findings to advance emotional requirements practices in software engineering. Future research may address (1) managing conflicting emotional requirements across users, (2) evaluating the value and impact of considering emotional requirements during the development and (3) Modeling and analyzing emotional requirements in relation to other requirements.

The conceptualization of enablers and constraints of in‐store buying as part of the affordances flow funnel process through scan and go apps

AbstractThis study explores the affordances of “scan and go” apps and their influence on the flow experience of retail customers to provide a unique insight into user–technology interaction. Through a constructivist ethnographic approach, the research examines how users' socially constructed perceptions and interpretations shape these interactions, and it emphasizes the role of the material and social environment. The research innovatively conceptualizes affordances as a flow process; it introduces an affordances flow funnel that encompasses three distinct stages: perceived affordances, actualized affordances, and affordance dichotomy. By following this process, the study improves understanding of user emotions and behaviors that range from apathy to excitement, from gratification to provocation, and from abandonment to absorption. Findings underline the importance of equipping users with support to navigate technological and environmental constraints, thereby ensuring successful affordance actualization. The research contributes to the literature by revealing a new affordance type, namely affordance dichotomy, and offers valuable insights for marketers and developers to enhance user experiences and absorption behavior. Recognizing its focus on scan and go apps within supermarket contexts, the study invites future research to extend this understanding to different contexts and technologies.

Scalable Action Mining Hybrid Method for Enhanced User Emotions in Education and Business Domain

Education sector ,Business field,Medical domain and SocialMedia,huge amounts of data in a single day . Mining this data can provide a lot of meaningful insights on how to improve user experience in social media, users engage in these domains collect and cherish the data as they hope to find patterns and trends and the golden nuggets that help them to accomplish their goal. For example: How to improve student learning; how to increase business profit ability; how to improve user experience in social media; and how to heal patients and assists hospital administrators. Action Rule Miningmines actionable patterns which are hidden in various datasets. Action Rules provide actionable suggestions on how to change the state of an object from an existing state to a desired state for the benefit of the user. There are two major frameworks in the literature of Action Rule mining namely Rule-Based method where the extraction of Action Rules is dependent on the pre-processing step of classification rule discovery and Object-Based method where it extracts the Action Rules directly from the database without the use of classification rules.Hybrid Action rule mining approach combines both these frame works and generates complete set of Action Rules. The hybrid approach shows significant improvement in terms computational performance over the Rule-Based and Object-Based approach. In this work we propose a novel Modified Hybrid Action rule method with Partition Threshold Rho, which further improves the computational performance with large datasets.

Development of the Engagement Playability and User eXperience (EPUX) Metric for 2D-Screen and VR Serious Games: A Case-Study Validation of Hellblade: Senua’s Sacrifice

Research into the design of serious games still lacks metrics to evaluate engagement with the experience so that users can achieve the learning aims. This study presents the new EPUX metric, based on playability and User eXperience (UX) elements, to measure the capability of any serious game to maintain the attention of players. The metric includes (1) playability aspects: game items that affect the emotions of users and that constitute the different layers of the game, i.e., mechanics, dynamics and aesthetics; and (2) UX features: motivation, meaningful choices, usability, aesthetics and balance both in the short and in the long term. The metric is also adapted to evaluate virtual reality serious games (VR-SGs), so that changes may be considered to features linked to playability and UX. The case study for the assessment of the EPUX metric is Hellblade, developed in two versions: one for 2D-screens and the other for VR devices. The comparison of the EPUX metric scores for both versions showed that (1) some VR dynamics augmented the impact of gameplay and, in consequence, engagement capacity; and (2) some game design flaws were linked to much lower scores. Among those flaws were low numbers of levels, missions, and items; no tutorial to enhance usability; and lack of strategies and rewards to increase motivation in the long term.

Voice Fence Wall: User-optional voice privacy transmission

Sensors are widely applied in the collection of voice data. Since many attributes of voice data are sensitive such as user emotions, identity, raw voice collection may lead serious privacy threat. In the past, traditional feature extraction obtains and encrypts voice features that are then transmitted to upstream servers. In order to avoid sensitive attribute disclosure, it is necessary to separate the sensitive attributes from non-sensitive attributes of voice data. Motivated by this, user-optional privacy transmission framework for voice data (called: Voice Fence Wall) is proposed. Firstly, we provide user-optional, which means users can choose the attributes (sensitive attributes) they want to be protected. Secondly, Voice Fence Wall utilizes minimum mutual information (MI) to reduce the correlation between sensitive and non-sensitive attributes, thereby separating these attributes. Finally, only the separated non-sensitive attributes are transmitted to the upstream server, the quality of voice services is satisfied without leaking sensitive attributes. To verify the reliability and practicability, three voice datasets are used to evaluate the model, the experiments demonstrate that Voice Fence Wall not only effectively separates attributes to resist attribute inference attacks, but also outperforms related work in terms of classification performance. Specifically, our framework achieves 89.84 ​% accuracy in sentiment recognition and 6.01 ​% equal error rate in voice authentication.

Mental health chatbot therapist

Mental health illness refers to conditions that affect thinking, feeling, mood, and behavior. These conditions may be occasional or long-lasting. Some examples of mental illness include depression, anxiety disorder, and addictive behaviors, to name a few. Mental health conditions can be caused by various factors, including substance abuse, psychological trauma, unhealthy habits, and genetics. Symptoms of mental illness may include feelings of sadness or hopelessness, extreme mood changes, excessive anger, hostility, and even violence. Individuals with mental health issues often require the assistance of a psychiatrist to undergo therapy, which is typically conducted face-to-face. This in- person approach to therapy can present several challenges, such as high costs, a shortage of psychiatric specialists in hospitals, and the reluctance of some patients to engage in the therapeutic process. Given these drawbacks associated with traditional face-to-face therapy, the concept of creating a mental health chatbot therapist emerged. This chatbot is a computer program designed to simulate conversation with human users, often utilizing Natural Language Processing (NLP) to interpret inputs and generative AI to automate responses. The chatbot aims to provide an interactive conversational experience between the user and the machine, facilitating both written and spoken communication. In this system, there is a user module that requires registration for access. After logging into their accounts, users can begin chatting with the chatbot. The Mental Health Chatbot System operates as a conversational agent, functioning like a real- time therapist that analyzes the user's emotions with each interaction and offers appropriate responses and feedback.

PROSPECTS OF USING REMOTE TEACHING IN SCHOOL EDUCATION – RESEARCH FINDINGS

PROSPECTS OF USING REMOTE TEACHING IN SCHOOL EDUCATION – RESEARCH FINDINGS

Fine-tune BERT based on Machine Learning Models For Sentiment Analysis

Sentiments Analysis is a technique applied to examine emotions expressed by the users of social networks where they massively convey their opinions and permanently share their feelings. Therefore, its main objective is to determine the emotional attitude of a discourse by classifying it into different categories as positive, negative or neutral. However, in some research works, the authors failed to accurately specify the sentiments from the context due to the varying text lengths and the presence of equivocal emotional cues. This paper sets forth the deployment and assessment of the capabilities of applying machine learning sentiment analysis techniques using a publicly available IMDB dataset. Notably, this dataset encompasses numerous instances of irony and sarcasm. Lately, the Bidirectional Encoder Representations from Transformers model (BERT) has showcased its efficacy in the domain of text sentiment analysis classification. However, there remains a scope for enhancing the accuracy of SA. In the current study, BERT, and Word2vec are utilized to extract contextual sentences embeddings, while Support Vector Machine (SVM) is used to classify the emotions of the social networks users. To enhance SVM accuracy, the genetic algorithm is integrated to optimize its hyper-parameters. The comparative study revealed that the developed technique applied on IMDB dataset outperforms other baseline machine learning techniques.

Some considerations on the emotions of heritage language learners, teachers, and users

Some considerations on the emotions of heritage language learners, teachers, and users

A social network security user recommendation algorithm based on community user emotions

A social network security user recommendation algorithm based on community user emotions

Research on Design Strategies for Exploring User Emotions and Experiences from the Perspective of Product Morphology and Semantics

Research on Design Strategies for Exploring User Emotions and Experiences from the Perspective of Product Morphology and Semantics

소셜미디어 품질이 긍정적 감정, 행동의도에 미치는 영향

Purpose With the recent challenges brought about by COVID-19, many individuals are turning their focus inward, seeking personal happiness. This shift has also influenced tourism trends, where travel experiences are now more about catering to individual preferences and facilitating shared experiences with like-minded travelers, addressing the inherent need for connection and communication. This evolving dynamic has spotlighted the role of social media quality as a pivotal marketing tool in the tourism industry. In this context, this research sought to explore the causal relationship between the quality of social media and the positive emotions, as well as the behavioral intentions of tourists.
 Methods In order to achieve the proposal of this study, the survey result of 260 questionnaires collected from tourists who watched a YouTube channel managed by the city of Gwangju. In addition, this study had been distributed over a three week period beginning from July 30th to July 10th, 2023.
 Results The analysis results are as follows. First, among the factors of social media quality that service quality and emotional quality were positively related to positive emotion. Second, it was established that positive emotion obtained a positive relation behavioral intention.Third, positive emotions were found to have a unique positive mediating effect on the social media quality between behavioral intention.
 Conclusion The findings of this analysis highlight the importance of effectively managing the quality of social media content. It underscores the value of fostering positive user emotions and intentions, suggesting a proactive approach is essential.

Modelling conversational agent with empathy mechanism

Empathy mechanism in communication is the cornerstone for effective and meaningful interaction. Establishing an empathy mechanism in conversation agent (CA) requires accurate recognition of users’ emotions to facilitate generates appropriate empathetic responses. Therefore, we proposed a Multimodal Emotion Recognition Model (MERM) to recognizes a user’s emotional state from multimodal data (audio, facial expressions, and conversation text) during conversation, and an Interactive Empathetic Conversation Model (IECM) to generate empathetic responses based on the MERM. Comparative and ablation study results indicated that the proposed models outperform existing methods in recognizing the user’s emotions and generating appropriate empathetic responses. We also conducted an experiment study, the results indicated that the CA significantly enhances the user’s emotional experience.

Analisis Website Pusat Pengembangan Karir Berdasarkan Pengalaman Pengguna: Pendekatan User Experience Questionnaire (UEQ)

Pembangunan National “Veteran” University of East Java has developed the Career Development Center website as a job vacancy information center to prepare graduates who are tough, innovative, and creative in the world of work. The Career Development Center website makes it easier to search for and find job opportunities that suit the field of study, skills, and interests of college graduates. A positive user experience in using the Career Development Center website can increase satisfaction and optimize career searches, so it is important to carry out measurements systematically and objectively. This study aims to measure the level of experience of users of the Career Development Center website using the User Experience Questionnaire (UEQ). UEQ measurements on the Career Development Center website focus on gaining an understanding of the Attractiveness, Pragmatic quality, and Hedonic quality aspects. UEQ provides measurements of technical and non-technical aspects related to the user's emotion or perception of pleasure. The results of measuring user experience on the Career Development Center website can help identify trends and changes in user needs so that they can adapt and develop relevant and innovative features. This study uses quantitative data by distributing 26 items of the UEQ questionnaire online to 350 alumni. The research results stated that the attractiveness aspect had the highest average value of 1.94. These results prove that the Career Development Center website has a high attractiveness value for users, thereby increasing the opportunity for continuous use. Apart from that, based on the high average stimulation value of 1.930, it can be proven that graduates who use the Career Development Center website get large benefits related to alumni career development.

Beyond Words: An Intelligent Human-Machine Dialogue System with Multimodal Generation and Emotional Comprehension

Intelligent service robots have become an indispensable aspect of modern-day society, playing a crucial role in various domains ranging from healthcare to hospitality. Among these robotic systems, human-machine dialogue systems are particularly noteworthy as they deliver both auditory and visual services to users, effectively bridging the communication gap between humans and machines. Despite their utility, the majority of existing approaches to these systems primarily concentrate on augmenting the logical coherence of the system’s responses, inadvertently neglecting the significance of user emotions in shaping a comprehensive communication experience. To tackle this shortcoming, we propose the development of an innovative human-machine dialogue system that is both intelligent and emotionally sensitive, employing multimodal generation techniques. This system is architecturally comprised of three components: (1) data collection and processing, responsible for gathering and preparing relevant information, (2) a dialogue engine, which generates contextually appropriate responses, and (3) an interaction module, responsible for facilitating the communication interface between users and the system. To validate our proposed approach, we have constructed a prototype system and conducted an evaluation of the performance of the core dialogue engine by utilizing an open dataset. The results of our study indicate that our system demonstrates a remarkable level of multimodal generation response, ultimately offering a more human-like dialogue experience.

A Dynamic Emotional Propagation Model over Time for Competitive Environments

Emotional propagation research aims to discover and show the laws of opinion evolution in social networks. The short-term observation of the emotional propagation process for a predetermined time window ignores situations in which users with different emotions compete over a long diffusion time. To that end, we propose a dynamic emotional propagation model based on an independent cascade. The proposed model is inspired by the interpretable factors of the reinforced Poisson process, portraying the “rich-get-richer” phenomenon within a social network. Specifically, we introduce a time-decay mechanism to illustrate the change in influence over time. Meanwhile, we propose an emotion-exciting mechanism allowing prior users to affect the emotions of subsequent users. Finally, we conduct experiments on an artificial network and two real-world datasets—Wiki, with 7194 nodes, and Bitcoin-OTC, with 5881 nodes—to verify the effectiveness of our proposed model. The proposed method improved the F1-score by 3.5% and decreased the MAPE by 0.059 on the Wiki dataset. And the F1-score improved by 0.4% and the MAPE decreased by 0.013 on the Bitcoin-OTC dataset. In addition, the experimental results indicate a phenomenon of emotions in social networks tending to converge under the influence of opinion leaders after a long enough time.