Emotion Identification Research Articles

Phototherapy as the tool of emotional identification development of adolescents

The results of the empirical stage statistically proved that phototherapy is one of the effective tools of psycho-pedagogical assistance to adolescents who experience difficulties associated with emotional identification. We diagnosed the level of formation of this phenomenon within the sample of 240 respondents who are university and school students aged 17 to 20 years female and male. A number of psycho-diagnostic methods were used in the study: the method 'Toron to alexithymia scale (TAS-26)” and the questionnaire “self-attitude.” Summarizing the results, we state that most of the studied sample is in a state of emotional well-being but emotional discomfort occurs among some respondents with certain levels of propensity for alexithymia (22.5%). The research proves the positive impact of phototherapy on adolescents' emotional identification development through photographing and creating visual pictures or direct perception and interpretation of images.

Personal Experience Sharing Cat Meme Videos among Contemporary Chinese Young People: Take the Bilibili Platform as an Example

Abstract: This study explores the phenomenon of personal experience-sharing cat meme videos among contemporary Chinese young people on the Bilibili platform. By employing qualitative content and discourse analysis on two popular cat meme videos and their comments, this research delves into how these videos foster empathetic communication, thereby forming an interactive connection between creators and audiences. The research finds that the cat meme video connects the real world with the video world through the means of cat meme symbol, scene construction, narrative method, and atmosphere creation. The cat meme video absorbs the audiences emotional perception and identification, so as to promote the audiences emotional fit with the cat meme video and thus trigger the audiences empathy. The findings indicate that viewers are not merely passive recipients but active participants who, through their interactions, enhance the emotional transmission of the videos. This two-way empathy communication leads to the establishment of a supportive online community, where individuals share similar experiences, offer advice, and provide emotional support. Consequently, cat meme videos on Bilibili serve as a medium for young people to express their social emotions, creating a vibrant and emotionally connected online community.

Promoting social and emotional learning (SEL) in settings through emotion coaching practice: a model of engagement for practitioner use

ABSTRACT Emotion Coaching (EC) is a universal, relational approach focussed on promoting emotional wellbeing through attuned relationships. Adults coregulate children to understand different emotions they experience, why they occur and how to manage them. EC was introduced into UK educational settings in 2010 to promote children’s social and emotional learning (SEL) and build resiliency. With evidence of child and practitioner benefit, it is an adaptable ‘technique’ to manage dysregulated children and emotional situations. This paper, reports on educational practitioners’ EC experience. An inductive study, using semi-structured interviews, captured experiences and perceptions of 21, EC trained practitioners. Using constructivist grounded theory, analysis identified the fundamental role of the practitioner’s emotional identity and emotional awareness; recognised practice integration was influenced by practice opportunity and reflection with colleagues and acknowledged the need for managerial support to normalise and sustain EC in settings. With use, practitioner emotional awareness increased, suggesting modification in emotional identity, creating the opportunity to reaffirm, reconsider and re-engage in practice. Integration of EC practice was viewed as an ongoing journey, being described as ‘a way of being and a way of becoming’. The model of EC engagement conceptualised practice integration as a recursive cycle, starting and returning to the practitioner, with sequential, incremental stages. Through attuned child and adult interactions, EC confers, normalises and sustains symbiotic advantage. The model proposed is a practical tool, to guide planning, delivery and monitoring of practice integration.

Indie Games for Ethical and Socioemotional Development in Secondary Education

Video games, particularly those developed in the independent scene, are a form of media with great educational possibilities. This work in progress will be completed in three years and it aims to improve teachers video game literacy and help them implementing activities for ethical and socioemotional development (European key competencies 5 and 6) through indie games. Therefore, through design-based research and mixed-method designs, it aims to achieve three goals: (O1) identify the perception and meaning-making processes that teachers and students have about videogames and learning through video games; (O2) design a measurable training program for teachers around the educational possibilities of indie games and help them implement activities for ethical and socioemotional development; and (O3) evaluate the programs impact based on the indicators of key competencies 5 and 6. The project is in its first phases and it will be developed in the next three years. First, representative samples of each agent will be accessed to analyze their beliefs, types of knowledge, and practical experience in the field. Subsequently, four independent video games that promote key competencies 5 and 6 will be selected, and classroom activities will be designed for each of them. Next, teachers from four high schools in Spain (Cantabria, Baleares, Andalucía and Catalonia) will be trained in the use of the educational resources, and they will be accompanied in its implementation with students. Finally, the programs impact on students will be evaluated based on various psychological variables associated with key competences 5 and 6 (e.g., empathy, emotional identification, civic skills, etc.) as well as the participants perceptions of the experience, enabling the generation of design principles for future iterations and for the programs scalability.

Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health

A person's Mental Health (MH) dramatically influences their complete evolution in life, including their cognitive, emotional, and psychomotor components. A person with good MH is content with life and can be creative, learn new things, and take risks to accomplish more significant objectives. Currently, college students are dealing with MH concerns for various causes, which affect their academic performance and significantly contribute to poor academic results. Therefore, encouraging MH in college students presents a significant problem for educators, parents, teacher educators, and governments. Adolescence is a crucial and delicate time characterized by considerable physical, emotional, social, and religious changes. The physical, social, and psychological facets of an individual's growth are laid out in this period, with mental health as a crucial factor in promoting these gains. Therefore, it becomes crucial for researchers to use Deep Learning (DL) algorithms to study the association between MH and vital psychological characteristics, such as emotional intelligence, personality traits, and intelligence. The personal aspects, namely personality, emotional intelligence, and MH are all related ideas that influence one another. Individuals must have mental well-being and emotional harmony to have a good personality. The current study uses DL techniques to investigate the relationship between college students' MH, emotional intelligence, and personality features. To perform a thorough study on emotion identification and Mental Health Prediction (MHP) among college students, this project investigates the integration of edge computing enabled by the Internet of Things (IoT) in the context of intelligent systems. Innovative treatments are urgently needed due to this population's rising prevalence of MH issues. This paper aims to continuously monitor and predict college students' MH using Edge Computing (EC) and IoT technology.

The Relationships Between Organizational Citizenship Behavior, Affective Commitment and Team Effectiveness in A Non-Profit Organization

Non-profit organizations play a crucial role in fostering healthy communities by providing vital services that contribute to economic stability and mobility. However, the effectiveness of these organizations hinges on the positive and engaged behavior of their employees. These organizations may face challenges in staffing and resource management that can hinder performance and funding, making team effectiveness and organizational citizenship behavior essential for success. This study investigates the relationships between organizational citizenship behavior (OCB), affective commitment, and team effectiveness in a non-profit organization based in Selangor. Utilizing a cross-sectional design and a quantitative approach, 118 employees across local and international branches of a non-profit organization participated in an online survey. Data analysis was conducted using SPSS version 26. The study found that conscientiousness, courtesy, and civic virtue are the dimensions of (OCB) that positively influence team effectiveness, while altruism and sportsmanship did not show a significant impact in the non-profit context. Conscientious employees who are diligent and responsible, along with those who exhibit courtesy by being considerate and communicative, and those who actively participate in organizational activities, contribute meaningfully to team success. Furthermore, affective commitment, or employees' emotional attachment and identification with the organization, was also found to enhance team effectiveness, highlighting the role of an engaged and committed workforce in driving positive outcomes.

Sentiment analysis in medication adherence: using ruled-based and artificial intelligence-driven algorithms to understand patient medication experiences.

Studies are exploring ways to improve medication adherence, with sentiment analysis (SA) being an underutilized innovation in pharmacy. This technique uses artificial intelligence (AI) and natural language processing to assess text for underlying feelings and emotions. This study aimed to evaluate the use of two SA models, Valence Aware Dictionary for Sentiment Reasoning (VADER) and Emotion English DistilRoBERTa-base (DistilRoBERTa), for the identification of patients' sentiments and emotions towards their pharmacotherapy. A dataset containing 320,095 anonymized patients' reports of experiences with their medication was used. VADER assessed sentiment polarity on a scale from - 1 (negative) to + 1 (positive). DistilRoBERTa classified emotions into seven categories: anger, disgust, fear, joy, neutral, sadness, and surprise. Performance metrics for the models were obtained using the sklearn.metrics module of scikit-learn in Python. VADER demonstrated an overall accuracy of 0.70. For negative sentiments, it achieved a precision of 0.68, recall of 0.80, and an F1-score of 0.73, while for positive sentiments, it had a precision of 0.73, recall of 0.59, and an F1-score of 0.65. The AUC for the ROC curve was 0.90. DistilRoBERTa analysis showed that higher ratings for medication effectiveness, ease of use, and satisfaction corresponded with more positive emotional responses. These results were consistent with VADER's sentiment analysis, confirming the reliability of both models. VADER and DistilRoBERTa effectively analyzed patients' sentiments towards pharmacotherapy, providing valuable information. These findings encourage studies of SA in clinical pharmacy practice, paving the way for more personalized and effective patient care strategies.

Data augmentation using a 1D-CNN model with MFCC/MFMC features for speech emotion recognition

Speech emotion recognition (SER) is attractive in several domains, such as automated translation, call centres, intelligent healthcare, and human–computer interaction. Deep learning models for emotion identification need considerable labelled data, which is only sometimes available in the SER industry. A database needs enough speech samples, good features, and a better classifier to identify emotions efficiently. This study uses data augmentation to enhance the amount of input voice samples and address the data shortage issue. The database capacity increases by adding white noise to the speech signals by data augmentation. In this work, the Mel-frequency Cepstral Coefficient (MFCC) and Mel-frequency Magnitude Coefficient (MFMC) features, along with a one-dimensional convolutional neural network (1D-CNN), are used to classify speech emotions. The datasets utilized to estimate the model's enactment were AESDD, CAFE, EmoDB, IEMOCAP, and MESD. The data augmentation with the 1D-CNN (MFMC) model performed best, with an average accuracy of 99.2% for AESDD, 99.5% for CAFE, 97.5% for EmoDB, 92.4% for IEMOCAP and 96.9% for the MESD database. The proposed 1D-CNN (MFMC) with data augmentation outperforms the 1D-CNN (MFCC) without data augmentation in emotion recognition.

Improvement of the human emotional state identification algorithm using MFCC

Improvement of the human emotional state identification algorithm using MFCC

Multi-Modal Emotion Recognition Feature Extraction and Data Fusion Methods Evaluation

Research into emotion detection is crucial because of the wide range of fields that can benefit from it, including healthcare, intelligent customer service, and education. In comparison to unimodal approaches, multimodal emotion recognition (MER) integrates many modalities including text, facial expressions, and voice to provide better accuracy and robustness. This article provides a historical and present-day overview of MER, focusing on its relevance, difficulties, and approaches. We examine several datasets, comparing and contrasting their features and shortcomings; they include IEMOCAP and MELD. Recent developments in deep learning approaches, particularly fusion strategies such as early, late, and hybrid fusion are covered in the literature review. Data redundancy, complicated feature extraction, and real-time detection are among the identified shortcomings. Our suggested technique enhances emotion recognition accuracy by using deep learning to extract features using a hybrid fusion approach. To overcome existing restrictions and advance the area of MER, this study intends to direct future investigations in the right direction. Examining various data fusion strategies, reviewing new methodologies in multimodal emotion identification, and identifying problems and research needs to make up the primary body of this work.

Самооценка преподавателя вуза в контексте современных медиа

Self-esteem and personal development depend on the social context. This article describes this correlation, within which personality affects the professional success. Such an impact is manifested in the work of a modern university educator, both in the media space and beyond. The social role of the teacher changes together with public relations: today, it is part of cultural capital and information economy. The professional self-esteem of a university educator, expressed in managerial metrics, determines their performance. The metrics can reveal a social interaction between the specialist and the society, where the specialist exchanges skills for reward, which affects the self-esteem and further interaction. The work of a university educator depends on the presence of the student as an actor, formal requirements of the university administration, and the ability to meet the students’ need for personal contact under standardization and mass production of information. The article gives university teaching personnel some recommendations on maintaining self-esteem connected with the development of emotional intelligence, impression management, and identification. The recommendations are supported by media presence cases.

Mentalized Affectivity, Helicopter Parenting, and Psychopathological Risk in Emerging Adults: A Network Analysis.

Emerging adulthood represents a critical stage characterized by heightened risks for anxiety, depression, and somatization symptoms development. Research has shown that difficulties in emotional identification, expression, and processing, as well as dysfunctional parenting styles, may exacerbate symptoms in emerging adults. The present study aimed at examining the interplay between mentalized affectivity (i.e., emotional identification, processing, and expression), helicopter parenting, and psychopathological risk in 913 Italian cisgender emerging adults (M = 24.34, SD = 2.81; 71.20% assigned female at birth), using network analysis. The results indicated moderate to strong associations between psychopathological symptoms, with emotional processing difficulties significantly associated with general anxiety, depression, and, to a lesser extent, somatization. Additionally, increased degrees of helicopter parenting from mothers were linked to increased psychopathology and higher emotional processing difficulties. These findings emphasize the importance of addressing the interconnection between symptoms and emotional processing to prevent and treat psychopathological risks in emerging adults. Moreover, interventions targeting intrusive and overprotecting parenting behaviors may promote well-being among emerging adults.

Cultivation of Youth Patriotism in the New Era: Current Situation, Objectives and Practical Approaches

Youth patriotism education is an important part of maintaining national unity and promoting the growth and success of young people to realize the great rejuvenation of the Chinese nation in the new era. However, youth patriotism education in the new era presents the following status quo: firstly, the education method and content are single and boring; secondly, it is deeply impacted by the negative information on the Internet and the invasion of diversified ideologies and cultures; and thirdly, the cultural self-confidence is weakened, and the influence of traditional culture is declining. Patriotic education for young people in the new era should aim at realizing young people's emotional identity, ideological identity and behavioral identity of knowledge and action. To this end, patriotic education in the new era can be adjusted from the adjustment of the form and content of patriotic education, strengthening the governance of multiple subjects in cyberspace, as well as basing on traditional culture and firming up cultural self-confidence, with a view to enhancing the effectiveness of patriotic education for young people in the new era.

Emotion–Identity–Agency Triangle in Practicum Experience: A Pre-service Second Language Teachers Development of Critical Emotional Reflexivity

Earlier research has established that language teachers’ identity work is intertwined with emotions and called for incorporating emotions in language teacher education. Yet, there is limited research investigating how teacher education programmes pedagogize identity, emotions and agency to help pre-service teachers gain critical emotional reflexivity. To respond to this gap in the literature, we pedagogized Kayi-Aydar’s emotion–identity–agency triangle via three teacher learning activities in a critical practicum course in a pre-service English language teaching programme in Turkey: reflective emotion diary; auto-ethnography; and critical issues analysis and advocacy project. In this study, we explore a Korean pre-service language teacher's (Iris's) case to demonstrate how she navigated her emotions, identity and agency throughout the practicum course with the help of these teacher learning activities and critical, dialogical supervision in our course. At the beginning of the term, Iris expressed anxiety due to her novice non-native pre-service teacher identity. Yet, as she gained critical emotional reflexivity, Iris could reflect on her emotions critically, transform her internalized deficit view of her identity as a novice non-native pre-service teacher and perform her multicultural and multilingual identity in her emerging pedagogy. We argue that findings from this study offer evidence that when intentionally and systematically supported by second language (L2) teacher educators through the design of teacher learning activities, pre-service L2 teachers can develop critical reflexivity of their emotions and emotion labour and recruit this awareness for teacher agency even over a short period of time and in contexts of power imbalance such as pre-service practicum experience. We offer recommendations for L2 teacher educators.

Emotional Education for Proper Money Management, Promoting Human Development

This study examines the relationship between emotional education and effective money management, with a particular focus on its impact on human development. It is proposed that the identification and proper management of emotions enables individuals to make more informed financial decisions and to create new economic opportunities. A review of the literature and an empirical analysis demonstrate that digital platforms can be effective in promoting educational content that addresses both emotional and financial backwardness in Mexico. The findings highlight the necessity of integrating emotional education into financial education programs to enhance the general well-being of the population. Furthermore, the “Puntoencomún” platform is presented as an innovative model to promote a positive vision of work and the development of emotional and financial skills.

Promoting emotion understanding in middle childhood: A systematic review of school-based SEL programs

Emotion understanding (EU) describes the ability to identify, interpret, and communicate about emotions, and is often targeted by social-emotional learning (SEL) programs. Still, the theoretical framing of SEL programs and their impact on specific areas of social-emotional development, such as EU, for different age bands is not always transparent. This systematic review synthesized emotion-focused content in SEL programs used in quantitative outcome studies in middle childhood to identify which EU components are targeted and examine content congruence with an integrated EU development model drawing on the Pons EU developmental model and the Crick and Dodge social-information processing (SIP) model that posits emotion identification as fundamental to social decision-making in childhood. A total of 38 programs for Grades 3 to 5 across 54 studies in 20 countries were reviewed. Program aims, lesson topics, and activities were extracted and mapped to a 10-component EU framework integrating the nine Pons model components (‘recognition, external cause, reminder, desire, belief, hiding, regulation, mixed, morality’) with one based on the SIP model (‘decision/action’). At least 87% of emotion-focused SEL content targeted EU components of recognition, regulation, and social decision-making. Findings indicate a good level of congruence between emotion-focused SEL program content and prevailing EU development models. Many programs emphasized the external causes of emotions, underscoring the importance of scenarios to explain emotions—discussed further in light of cross-cultural variation in emotion socialization. We encourage SEL intervention research to be more transparent in reporting SEL program content and activities to move toward causal explanations of program impact.

Emotion Detection using EEG Signals in Image and Video Processing: A Survey

Brain serves as the body's processing of knowledge and management centre. The central nervous system directly produces ElectroEncephaloGram (EEG) physiological signals, which are strongly associated with human emotions. In the upcoming years, there will be an increase in interest for identifying emotion using brain waves by EEG (ElectroEncephaloGraphy) signals. It takes efficient and effective signal processing and feature extraction techniques for detection of emotions from human biological brain signals. Current approaches gather valuable information from a fixed number of ElectroEncephaloGraphy (EEG) channels utilizing variety of methodologies. This work analyses the different difficulties and problems associated with EEG signals for emotion identification and provides a comprehensive summary of several contemporary approaches. Pre-processing, feature extraction, and categorization are the first steps in the process of recognizing emotions from EEG signals. The main goal of this survey is to sought and enhance brain signal-based emotion detection ability by comparing all novel and adaptive channel selection technique that recognize the distinct changes in brain activities that varies between individuals and emotional states.

Research on the Communication Effect of Digital Collection in Museum Based on the Analysis of Review Text

Museum digital collections have become a new form of museum communication, stimulating the public's interest in and love for cultural relics, and better promoting the dissemination of culture. This paper takes China's largest digital collection selling platform - whale probe as an example, selects the digital collections that have been on the hot search list of literature and culture and blogs for at least one consecutive month in the platform, grabs the largest sample size that can be grabs with the help of Fiddler and Python to analyze it, and applies text analysis to analyze the information dissemination effect of the digital collections of museums in terms of the depth of communication. The information dissemination effect of digital collections was analyzed in terms of communication depth. It is found that external image identity, internal emotional identity and economic value identity affect the identity of the audience group from the depth of communication.

Perceived Impacts of Tourism on Community Identity: Perspectives of Two Scottish Highland Communities

ABSTRACT The Scottish Highlands tourism industry has grown significantly due to the successful launch of the North Coast 500 route. This study investigates the perceived tourism impact on rural communities based on a survey and semi-structured interviews conducted in Elphin and Ullapool in 2021. By approaching community identity as a combination of emotional identity, functional identity, and community participation, the paper identifies key differences in the identities of two communities usually framed as belonging to one homogenous region. This provides host community perspectives on which aspects of their identities may be targeted to support effective tourism developments. We conclude that although the overall strength of both communities identities seem unaffected by growing tourism levels, they do shape their nature, particularly when different concerns intersect such as environmental degradation and limited affordable housing. Addressing resident concerns through meaningful contributions to decision-making processes is key to the success of regional tourism policies.

Analysis of factors influencing medical students’ learning engagement and its implications for teaching work—— a network analysis perspective

PurposeHigher medical education has always been a major project in the fields of education and health, and therefore, the quality of education has received much attention. Learning engagement has emerged as a significant indicator of teaching quality, attracting considerable research attention. This study aims to explore the relationship between medical students’ learning engagement and their sense of school belonging, professional identity, and academic self-efficacy.MethodsWe conducted an online survey using convenience sampling method with 311 medical students. We employed Revised version of the Utrech Work Engagement Scale-Student (UWES-S), Chinese version of the Psychological Sense of School Membership (PSSM) scale, Academic Self-Efficacy Scale, and the questionnaire of college students’ speciality identity for evaluation. Network analysis was used to analyze the relationships among these factors.ResultsMedical students’ overall performance in school showed a positive trend. However, there is still room for improvement. In the network structure of learning engagement and its influencing factors, the “emotional” aspect of professional identity (EI = 1.11) was considered to be an important node with strong centrality. And “academic competence self-efficacy” aspect of academic self-efficacy (BEI = 0.72) was considered an important node with strong transitivity.ConclusionDeepening medical students’ emotional identification with their profession and enhancing their confidence in their academic abilities may improve their learning engagement and educational quality.