User Emotions Research Articles

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

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.

Emotion Based Music Player

The work presents described the development of Emotion Based Music Player, which is a computer application meant for all type of users, specifically the music lovers. Due to the troublesome workloads in songs selection, most people will choose to randomly play the songs in the playlist. As a result, some of the songs selected not matching the users’ current emotion. Moreover, there is no commonly used music player which able to play the songs based on user’s emotion. The proposed model is able to extract user’s facial expression and thus detect user’s emotion. The music player in the proposed model will then play the songs according to the category of emotion detected. It is aimed to provide a better enjoyment to music lovers in music listening. The scope of emotions in the proposed model involve normal, sad, surprise and happy. The system involves the major of image processing and facial detection technologies. Facial expression is a easy way and most ancient way of expressing emotion, feelings and ongoing mood of the person. This model based on real time extraction of facial expression and identify the mood. In this project we are using Haar cascade classifier to extract the facial features based on the extracted features from Haar cascade, we are using COHN KANADE dataset to identify the emotion of user. If the user's detected emotion is neutral then the background will be detected and the music will play according to the background.

Impact of Appraisal Concerns on Marketing Information Sharing Behavior in Social Commerce: Integrating User Emotions and System Feedback Dynamics

This study, grounded in the theory of evaluation apprehension, aims to develop a comprehensive model that integrates user psychology, emotional dynamics, and system functionalities to examine user behavior in marketing information sharing. A methodological blend of online surveys and offline lab experiments is employed to explore the obstructive role of evaluation apprehension in the dynamics between perceived sharing value and the behavior of sharing marketing information. Additionally, the study investigates the operational mechanism of system feedback attributes - a distinct system characteristic - in the user sharing process, specifically focusing on its moderating effect on the inhibitory influence of evaluation apprehension. On a practical note, it provides strategic insights for businesses from the angles of user psychology, reward mechanism design, and system architecture to encourage more active user participation in sharing marketing information.

Analisis Sentimen SEA Games 2023 di Twitter Metode dengan Machine Learning

Sentiment analysis is a method used to analyze and identify the polarity (positive, negative, or neutral) of text or data related to a user's thoughts, opinions, or emotions. This method is widely used in various fields, including sentiment analysis on social media data. One very popular social media platform is Twitter. One of the biggest sporting events in Asia is the SEA Games, which are held every two years. SEA Games, many Indonesians expressed their opinions, support and emotions regarding the 2023 SEA Games on Twitter. Using supervised learning methods can provide valuable insight into how Indonesian society responds and reacts to this important sporting event. The results of this analysis can help related parties, including organizers and sponsors of the 2023 SEA Games, in understanding public sentiment, evaluating performance, and making better decisions in order to organize a successful sporting event. The results of the trials carried out by the SVM method had the best performance with an F-1 score of 61.53%.

Discovering e-commerce user groups from online comments: An emotional correlation analysis-based clustering method

Platform merchants mine user clusters and their characteristics to assist in precision marketing. In view of the information overload in e-commerce reviews, machine methods are needed to efficiently obtain clustering information from text. This study innovatively integrated the emotional correlation analysis model and Self-organizing Map (SOM) in application, to construct fine-grained user emotion vector based on review text and perform visual cluster analysis, which helped quickly mine user clustering and characteristics from review text. The result of empirical analysis based on real reviews of Amazon books showed that the proposed method had the average precision as 0.71, confirming that the clustering method integrating the emotional correlation analysis model and SOM could efficiently mine user groups and match appropriate marketing strategies, which will help platform merchants carry out precision marketing. The study makes contributions to the application and innovation of researches in the field of user clustering and e-commerce precision marketing.

Examining the Impact of Crowding Perception on the Generation of Negative Emotions among Users of Small Urban Micro Public Spaces

The improvement of urban congestion and the mood of the populace is vital for achieving the United Nations Sustainable Development Goals. This paper aims to test the hypothesis that crowding in urban micro public spaces (UMPSs) affects emotions, and two different methods were used: a psychological questionnaire to measure whether crowding in such spaces elicits negative emotional responses from users and a portable electroencephalography (EEG) device to explore emotional responses. This study was conducted in Shenyang, China, based on the city’s relevant policies and the type and number of micro spaces and micro public spaces, proving that Shenyang City has paid more attention to planning micro spaces and micro public spaces in its urban development. The results show that 1. UMPS crowding awakens negative emotions in users, which affects their intention to revisit the UMPS, and 2. If the UMPS is more attractive to users, it also improves the negative emotions due to crowding, which implies that the attractiveness of the environment does play an important moderating role. This study may inspire the construction concept of UMPSs in different countries and cities, complementing the planning of urban public spaces to provide more social equity. The creation of UMPS has a positive effect on neighborhood interactions, community network construction, and the reproduction of social capital, which can contribute to the sustainable development of cities.

ConCollA - A Smart Emotion-based Music Recommendation System for Drivers

Music recommender system is an area of information retrieval system that suggests customized music recommendations to users based on their previous preferences and experiences with music. While existing systems often overlook the emotional state of the driver, we propose a hybrid music recommendation system - ConCollA to provide a personalized experience based on user emotions. By incorporating facial expression recognition, ConCollA accurately identifies the driver’s emotions using convolution neural network(CNN) model and suggests music tailored to their emotional state. ConCollA combines collaborative filtering, a novel content-based recommendation system named Mood Adjusted Average Similarity (MAAS), and apriori algorithm to generate personalized music recommendations. The performance of ConCollA is assessed using various evaluation parameters. The results show that proposed emotion-aware model outperforms a collaborative-based recommender system.

Analysis of the evolving factors of social media users’ emotions and behaviors: a longitudinal study from China’s COVID-19 opening policy period

The outbreak of the COVID-19 pandemic has triggered citizen panic and social crises worldwide. The Chinese government was the first to implement strict prevention and control policies. However, in December 2022, the Chinese government suddenly changed its prevention and control policies and completely opened up. This led to a large-scale infection of the epidemic in a short period of time, which will cause unknown social impacts. This study collected 500+ epidemic-related hotspots and 200,000+ data from November 1, 2022, to March 1, 2023. Using a sentiment classification method based on pre-trained neural network models, we conducted inductive analysis and a summary of high-frequency words of various emotions. This study focuses on the inflection point of the emotional evolution of social media users and the evolution of “hot topic searches” events and emotional behavioral factors after the sudden open policy. Our research results show that, first of all, the positive emotions of social media users are divided into 4 inflection points and 5 time periods, and the negative emotions are divided into 3 inflection points and 4 time periods. Behavioral factors are different at each stage of each emotion. And the evolution patterns of positive emotions and negative emotions are also different. Secondly, the evolution of behavioral elements deserves more attention. Continue to pay attention: The treatment of diseases, the recovery of personal health, the promotion of festive atmosphere, and the reduction of publicity on the harm of “new crown sequelae and second infections” are the behavioral concerns that affect users’ emotional changes. Finally, it is necessary to change the “hot topic searches” event by guiding the user’s behavioral focus to control the inflection point of the user’s emotion. This study helps governments and institutions understand the dynamic impact of epidemic policy changes on social media users, thereby promoting policy formulation and better coping with social crises.