User Emotions Research Articles

Impact of user-generated content in digital platforms on purchase intention: the mediator role of user emotion in the electronic product industry

Impact of user-generated content in digital platforms on purchase intention: the mediator role of user emotion in the electronic product industry

Emotions, behaviors and places: Mapping sentiments with behaviors in Japanese tweets

Emotions are the basis of human behavior and an important part of spatial constructions. Despite numerous efforts to leverage spatio-temporal big data from social media to capture public sentiments, there is a lack of understanding of how individuals' behaviors play a role in people's sentiments at places they visit. Thus, this study aims to advance our understanding of the complex relationships between sentiments, behaviors, and places. Through a case study in Japan in 2021, we analyzed one year of countrywide geotagged social media by fine-tuning a pre-trained Japanese BERT model (accuracy: 74 %). The results emphasize the importance of understanding user emotions by considering people's behaviors, even within similar types of locations. Our research framework would be beneficial for urban planners and policymakers to enhance urban spaces.

BERTopic과 색채분석 기반 감성 추출을 이용한 관광지 추천 시스템 개발

This study aims to develop a personalized tourist destination recommendation system by analyzing the color and emotional factors of destination images. It evaluated and analyzed the emotional responses and color characteristics conveyed by images of natural resorts and urban tourist sites. A survey was conducted to collect users' emotional responses to these images, and a BERTopic model based on BERT was used to analyze text data from social media. The survey results provided a clear understanding of how users react to specific colors and emotional factors. For natural resorts, Green(G) was the most associated color, followed by Green-Yellow(GY) and Blue(B). In the case of urban tourist sites, Yellow-Red(YR) and Yellow(Y) were most commonly associated, followed by Blue-Green(BG) and Blue(B). Additionally, during the system's execution, the Bhattacharyya distance was used to measure color similarity between images, displaying images that match the user's emotions. Therefore, this study developed a recommendation system that considers the impact of destination images on users' emotions, proposing a new approach to better reflect users' emotional needs.

Towards Emotionally Intelligent Virtual Environments: Classifying Emotions through a Biosignal-Based Approach

This paper introduces a novel method for emotion classification within virtual reality (VR) environments, which integrates biosignal processing with advanced machine learning techniques. It focuses on the processing and analysis of electrocardiography (ECG) and galvanic skin response (GSR) signals, which are established indicators of emotional states. To develop a predictive model for emotion classification, we extracted key features, i.e., heart rate variability (HRV), morphological characteristics, and Hjorth parameters. We refined the dataset using a feature selection process based on statistical techniques to optimize it for machine learning applications. The model achieved an accuracy of 97.78% in classifying emotional states, demonstrating that by accurately identifying and responding to user emotions in real time, VR systems can become more immersive, personalized, and emotionally resonant. Ultimately, the potential applications of this method are extensive, spanning various fields. Emotion recognition in education would allow further implementation of adapted learning environments through responding to the current emotional states of students, thereby fostering improved engagement and learning outcomes. The capability for emotion recognition could be used by virtual systems in psychotherapy to provide more personalized and effective therapy through dynamic adjustments of the therapeutic content. Similarly, in the entertainment domain, this approach could be extended to provide the user with a choice regarding emotional preferences for experiences. These applications highlight the revolutionary potential of emotion recognition technology in improving the human-centric nature of digital experiences.

Emotional design for pro-environmental life: Visual appeal and user interactivity influence sustainable consumption intention with moderating effect of positive emotion

As the public pursues sustainable consumption patterns, pro-environmental mobile applications are being developed to promote green lifestyles. Ant Forest has attracted a large number of users to participate due to its gamified interactive design. The objectives of this study are to understand the process by which visual appeal and user interactivity in pro-environmental mobile applications influence the user's sustainable consumption intention and to analyse the moderating effect of the user's positive emotion on this process. The researchers constructed a theoretical framework based on the Stimulus-Organism-Response (S-O-R) theory in this study and used the Partial Least Squares Structural Equation Modelling (PLS-SEM) for quantitative analysis with an online questionnaire survey of 586 Ant Forest users. Findings for this study include: 1) Visual appeal shows a large positive effect on the perceived usefulness of Ant Forest; 2) Ant Forest's perceived usefulness shows a significant positive effect on the user's sustainable consumption intention; 3) Ant Forest's perceived usefulness fully mediates the relationship between visual appeal and the user's sustainable consumption intention; 4) Positive emotion moderates the relationship between perceived usefulness and sustainable consumption intention. This study improves the application of the S-O-R theory in emotional design research, while emphasising the importance of optimising the visual appeal of design elements and guiding users' positive emotions in the development of pro-environmental mobile applications. The findings of this study highlight the importance of incorporating positive emotions into visual design for designers and developers of pro-environmental applications, further clarifying the focus of emotional design.

Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study.

Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions. We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy. We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user's self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers. We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=-5.49, P<.001, Cohen d=0.36). Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.

Этническое мифотворчество в киберпространстве как фактор репрезентации этничности чувашей

The article is devoted to the study of Chuvash ethnic myth-making in cyberspace. The aim of the work is to determine its role as one of the factors of ethnicity representation. The study is based on the analysis of the relevant content presented on popular web platforms and social networks YouTube, Rutube, Zen, VKontakte, Odnoklassniki. The study identifies a certain section of content offered by search algorithms as the most viewed and included in thematic collections and playlists. The author examines the myth-making components of the Internet space, characterizes formats and plots, identifies their authors, and studies the audience's reaction to the content. Data analysis showed that ethnic myth-making has a positive effect on the representation of the Chuvash culture. Despite some inaccuracies in the information, such material generally evokes positive emotions in users. Information in video and text format is published on popular Russian platforms, making ethno-myth-creating content accessible to a wide audience. The myths created by the authors reflect their desire to show the antiquity and significance of the Chuvash people, which helps the Chuvash people to understand their identity, place and role in the world. Ethno-myth-creating content on the Internet increases interest in the Chuvash culture, promoting its popularization and representation.

Multimodal emotion recognition based on a fusion of audiovisual information with temporal dynamics

AbstractIn the Human-Machine Interactions (HMI) landscape, understanding user emotions is pivotal for elevating user experiences. This paper explores Facial Expression Recognition (FER) within HMI, employing a distinctive multimodal approach that integrates visual and auditory information. Recognizing the dynamic nature of HMI, where situations evolve, this study emphasizes continuous emotion analysis. This work assesses various fusion strategies that involve the addition to the main network of different architectures, such as autoencoders (AE) or an Embracement module, to combine the information of multiple biometric cues. In addition to the multimodal approach, this paper introduces a new architecture that prioritizes temporal dynamics by incorporating Long Short-Term Memory (LSTM) networks. The final proposal, which integrates different multimodal approaches with the temporal focus capabilities of the LSTM architecture, was tested across three public datasets: RAVDESS, SAVEE, and CREMA-D. It showcased state-of-the-art accuracy of 88.11%, 86.75%, and 80.27%, respectively, and outperformed other existing approaches.

A comparative analysis of game experience in treadmill running applications

Despite the growing popularity of treadmill running apps, comparative analyses between exergames and traditional running simulators in terms of user experience are scarce. This study aims to fill this gap by examining user experiences across three different treadmill running apps: Zwift (a running simulator), Ring Fit Adventure (a Nintendo Switch game), and Fitoon (a mobile-specific treadmill game). Using unique interaction mechanisms-pedometer, leg-mounted controller with Ring-Con, and smartwatch, respectively, each app is evaluated through the In-Game version of the Game Experience Questionnaire. Results indicate that exergames, particularly Fitoon, significantly improve Competence, Immersion, and game flow compared to traditional treadmill running activities. However, contrary to expectations, the use of a smartwatch for interaction did not significantly decrease levels of Negative Affect. This finding underscores the complexity of the relationship between interaction mechanisms and their impact on user emotions within treadmill running applications. By providing a comprehensive analysis of user experiences, this study provides insight into the potential benefits and limitations of different interaction systems and platforms, providing valuable information for the development of more emotionally engaging and rewarding treadmill running apps. crucial.

Exploring the Effects of Multi-Factors on User Emotions in Scenarios of Interaction Errors in Human–Robot Interaction

Exploring the Effects of Multi-Factors on User Emotions in Scenarios of Interaction Errors in Human–Robot Interaction

Empathic Conversational Agent Platform Designs and Their Evaluation in the Context of Mental Health: Systematic Review.

The demand for mental health (MH) services in the community continues to exceed supply. At the same time, technological developments make the use of artificial intelligence-empowered conversational agents (CAs) a real possibility to help fill this gap. The objective of this review was to identify existing empathic CA design architectures within the MH care sector and to assess their technical performance in detecting and responding to user emotions in terms of classification accuracy. In addition, the approaches used to evaluate empathic CAs within the MH care sector in terms of their acceptability to users were considered. Finally, this review aimed to identify limitations and future directions for empathic CAs in MH care. A systematic literature search was conducted across 6 academic databases to identify journal articles and conference proceedings using search terms covering 3 topics: "conversational agents," "mental health," and "empathy." Only studies discussing CA interventions for the MH care domain were eligible for this review, with both textual and vocal characteristics considered as possible data inputs. Quality was assessed using appropriate risk of bias and quality tools. A total of 19 articles met all inclusion criteria. Most (12/19, 63%) of these empathic CA designs in MH care were machine learning (ML) based, with 26% (5/19) hybrid engines and 11% (2/19) rule-based systems. Among the ML-based CAs, 47% (9/19) used neural networks, with transformer-based architectures being well represented (7/19, 37%). The remaining 16% (3/19) of the ML models were unspecified. Technical assessments of these CAs focused on response accuracies and their ability to recognize, predict, and classify user emotions. While single-engine CAs demonstrated good accuracy, the hybrid engines achieved higher accuracy and provided more nuanced responses. Of the 19 studies, human evaluations were conducted in 16 (84%), with only 5 (26%) focusing directly on the CA's empathic features. All these papers used self-reports for measuring empathy, including single or multiple (scale) ratings or qualitative feedback from in-depth interviews. Only 1 (5%) paper included evaluations by both CA users and experts, adding more value to the process. The integration of CA design and its evaluation is crucial to produce empathic CAs. Future studies should focus on using a clear definition of empathy and standardized scales for empathy measurement, ideally including expert assessment. In addition, the diversity in measures used for technical assessment and evaluation poses a challenge for comparing CA performances, which future research should also address. However, CAs with good technical and empathic performance are already available to users of MH care services, showing promise for new applications, such as helpline services.

Research on Sustainable Form Design of NEV Vehicle Based on Particle Swarm Algorithm Optimized Support Vector Regression

With the growing emphasis on eco-friendly and sustainable development concepts, new energy vehicles (NEVs) have emerged as a popular alternative to traditional fuel vehicles (FVs). Due to the absence of an internal combustion engine, electric vehicles (EVs) do not require a front air intake grille, allowing for a more minimalist and flexible design. Consequently, aligning EV styling with users’ visual cognition and emotional perception is a critical objective for automakers and designers. In this study, we establish the mapping relationship between users’ emotional cognition and NEV styling design based on experimental data. We introduce Particle Swarm Optimization Support Vector Regression (PSO-SVR) into the perceptual engineering (KE) research process to predict user emotions using Support Vector Regression (SVR). To optimize the three hyperparameters (penalty coefficient C, RBF kernel function parameter γ, and insensitivity loss coefficient ε) of the SVR model, we utilize the Particle Swarm Optimization (PSO) algorithm. The results indicate that the proposed PSO-SVR model outperforms traditional SVR and BPNN models in predicting NEV user emotions. This model effectively captures the nonlinear relationship between battery electric vehicle (BEV) morphological features and users’ emotional cognition, providing a novel method for enhancing NEV design. The results of this research are expected to drive design innovation and technological advancement in the new energy vehicle industry, contributing to the achievement of the ambitious goal of global eco-friendliness and sustainable development.

Conversational Breakdown in a Customer Service Chatbot: Impact of Task Order and Criticality on User Trust and Emotion

While chatbots are increasingly used for customer service, there is a knowledge gap concerning the impact of Conversational Breakdown in such chatbot interactions. In a 2x4 factorial design online experiment, we studied how Conversational Breakdown impacts user emotion and trust in a chatbot for customer service, given variations in task criticality and breakdown task order. Here, 257 participants were randomly assigned to complete high- or low-criticality tasks with a prototype chatbot for customer service, experiencing Conversational Breakdown for the first, second, third, or none of their tasks. The task set was decided from a 63-participant pre-study. We found significant impact of Conversational Breakdown, including a marked order effect on overall trust, as well as a bounce-back effect on task-specific trust and emotion after subsequent successful task completion. We found no post-interaction effect of Task Criticality. Based on our findings, we discuss theoretical and practical implications and suggest future research.

A Study on User Emotion by Mobile Phone Design Type: Focusing on Galaxy Z Flip, Galaxy Z Fold, I-Phone Series

A Study on User Emotion by Mobile Phone Design Type: Focusing on Galaxy Z Flip, Galaxy Z Fold, I-Phone Series

An Elderly-Oriented Form Design of Low-Speed New Energy Vehicles Based on Rough Set Theory and Support Vector Regression

With the current trend of social aging, the travel needs of the elderly are increasingly prominent. As a means of urban transportation, low-speed new energy vehicles (NEVs) are widely used among the elderly. Many studies are devoted to exploring the function of cars and the travel modes that meet the needs of older people. However, in addition to product performance, the Kansei needs of users also play a key role in communication between enterprises and users. Therefore, the problem of how to improve car shapes in the initial stage of design to meet the Kansei needs of elderly users remains to be solved. In order to fill this gap, the design of low-speed NEVs are selected as the study objects so as to explore the relationship between the visual perception of elderly users and car design; thus, a design method for the form of elderly-oriented cars is proposed. Firstly, using the research framework of Kansei engineering, factor analysis is used to cluster elderly-oriented Kansei factors. Second, the cars’ appearances are deconstructed by morphological analysis, and the key design features affecting elderly-oriented satisfaction are identified by a rough set attribute reduction algorithm. Finally, support vector regression is used to establish a mapping model of elderly-oriented Kansei factors and the key design features to predict the elderly-oriented form design of optimal low-speed NEVs. The research results show that selecting “Hub6”, “Headlight9”, “Car side view2”, “Rearview mirror9”, and “Front door10” in the form deconstruction table for low-speed NEVs can elicit optimal emotions in elderly users. The research results enable enterprises to more effectively understand the emotional cognition of elderly users related to the form of low-speed NEVs and improve the purchase desire and satisfaction of elderly users, providing references and guidance for the elderly-oriented design and development of intelligent transportation tools.

Label-refined EEG emotion classification based on intelligent media communication

With the development of cognitive computing and intelligent media communication, recognizing the users emotion using electroencephalography (EEG) has garnered increasing attention. However, building a cross-subject emotion recognition model with good generalization performance is really difficult. To achieve remarkable performance, a label-refined domain adversarial neural network is designed and EEG samples are subdivided into 6 groups according to the differences in users' brain activities under different movie stimuli. Based on the experiment of SEED and SEED-IV datasets, the classification accuracy of our method reaches 93.354.14% and 84.086.01%, achieving the state-of-the-art performance. Experiments show that using different movie stimuli to refine labels can achieve efficient cross-subject emotion recognition.

An emotion-sensitive dialogue policy for task-oriented dialogue system

Reinforcement learning (RL) is an effective method in training dialogue policies to steer the conversation towards successful task completion. However, most RL-based methods only rely on semantic inputs that lack empathy as they ignore the user emotional information. Moreover, these methods suffer from delayed rewards caused by the user simulator returning valuable results only at dialogue end. Recently, some methods have been proposed to learn the reward function together with user emotions, but they omit considering user emotion in each dialogue turn. In this paper, we proposed an emotion-sensitive dialogue policy model (ESDP), it incorporates user emotions information into dialogue policy and selects the optimal action by the combination of top-k actions with the user emotions. The user emotion information in each turn is used as an immediate reward for the current dialogue state to solve sparse rewards and the dependency on termination. Extensive experiments validate that our method outperforms the baseline approaches when combined with different Q-Learning algorithms, and also surpasses other popular existing dialog policies’ performance.

An AI-Powered Product Identity Form Design Method Based on Shape Grammar and Kansei Engineering: Integrating Midjourney and Grey-AHP-QFD

Product Identity (PI) is a strategic instrument for enterprises to forge brand strength through New Product Development (NPD). Concurrently, facing increasingly fierce market competition, the NPD for consumer emotional requirements (CRs) has become a significant objective in enterprise research and development (R&amp;D). The design of new product forms must ensure the continuity of PI and concurrently address the emotional needs of users. It demands a high level of experience from designers and significant investment in R&amp;D. To solve this problem, a generative and quantitative design method powered by AI, based on Shape Grammar (SG) and Kansei Engineering (KE), is proposed. The specific method is as follows: Firstly, representative products for Morphological Analysis (MA) are selected, SG is applied to establish initial shapes and transformation rules, and prompts are input into Midjourney. This process generates conceptual sketches and iteratively refines them, resulting in a set of conceptual sketches that preserve the PI. Secondly, a web crawler mines online reviews to extract Kansei words. Factor Analysis (FA) clusters them into Kansei factors, and the Grey Analytic Hierarchy Process (G-AHP) calculates their grey weights. Thirdly, after analyzing the PI conceptual sketches for feature extraction, the features are integrated with CRs into the Quality Function Deployment (QFD) matrix. Experts evaluate the relationships using interval grey numbers, calculating the optimal ranking of PI Engineering Characteristics (PIECs). Finally, professional designers refine the selected sketches into 3D models and detailed designs. Using a Chinese brand as a case study, we have designed a female electric moped (E-moped) to fit the PI and users’ emotional needs. Through a questionnaire survey on the design scheme, we argue that the proposed innovative method is efficient, applicable, and effective in balancing the product form design of PI and user emotions.

Towards Reducing Continuous Emotion Annotation Effort During Video Consumption: A Physiological Response Profiling Approach

Emotion-aware video applications (e.g., gaming, online meetings, online tutoring) strive to moderate the content presentations for a more engaging and improved user experience. These services typically deploy a machine-learning model that continuously infers the user's emotion (based on different physiological signals, facial expressions, etc.) to adapt the content delivery. Therefore, to train such models, the emotion ground truth labels also need to be collected continuously. Typically, those are collated as emotion self-reports from users in a continuous manner (using an auxiliary device such as a joystick) when they watch some videos. This process of continuous emotion annotation not only increases the cognitive load and survey fatigue but also significantly deteriorates the viewing experience. To address this problem, we propose a framework, PResUP that probes a user for emotion self-reports opportunistically based on the physiological response variations of the user. Specifically, the framework implements a sequence of phases - (a) user profile construction based on physiological responses, (b) similar user clustering based on the user profile, and (c) training a parameterized activation-guided LSTM (Long short-term memory) model by sharing data among similar users to detect the opportune self-report collection moments. All these steps together help to reduce the continuous emotion annotation overhead by probing at the opportune moments without compromising the annotation quality. We evaluated PResUP by conducting a user study (N=36) during which the participants watched eight videos, and their physiological responses and continuous emotion self-reports were recorded. The key results from this evaluation reveal that PResUP reduces the annotation overhead by reducing the probing rate by an average of 34.80% and detects the opportune probing moments with an average TPR of 80.07% without compromising the annotation quality. Motivated by these findings, we deployed PResUP by performing a follow-up user study (N=18). In this deployment scenario, we also obtained similar performance in terms of the probing rate reduction (average reduction of 38.05%), and opportune moment detection performance (average TPR of 82.26%). These findings underscore the utility of PResUP in reducing the continuous emotion annotation effort during video consumption.

HUBUNGAN PENGGUNAAN MEDIA SOSIAL DI INDONESIA DENGAN PENDEKATAN COMMUNICATION PRIVACY MANAGEMENT (CPM) GUNA MENINGKATKAN SELF DISCLOSURE ON SOCIAL MEDIA

This research is motivated by the fact that many social media are used very widely in the context of Self Disclosure which can fulfill the emotions of users on social media, but is accompanied by risks that can be detrimental to the user. The aim of this research is to provide knowledge, understanding and information about problems for users of social media platforms (Facebook, Instagram, Twitter (X), Tiktok, Youtube) in Indonesia. A total of 130 respondents participated in the survey who were users or who had used social media in Indonesia. Data were processed and analyzed using SPSS and AMOS using Structural Equation Modeling (SEM).