Emotional Model Research Articles

Social media users’ affective, attitudinal, and behavioral responses to virtual human emotions

Virtual humans’ communication of emotions in social media posts are considered heuristics of human behavior. The current study extends the Cognitive-Functional Model of Emotions to virtual influencers and investigates how social media users engage with virtual human emotions. In an experiment (N = 362), non-emotional and emotional (i.e., happy, sad, and lust) social media posts are examined for their influence on Generation Z and Millennial (18–40 years old) users’ affective responses and subsequent attitudinal and behavioral outcomes within the moderation of eeriness based on the uncanny valley paradigm. Findings indicate affective responses and attitudes are serial mediators between a virtual human’s display of emotion and social media users’ behavioral intentions, and eeriness was a significant moderator. Lust has the strongest effect, followed by happiness, sadness, and no emotion. This research elevates the Cognitive-Functional Model to discrete virtual human emotions with approach tendencies. Implications for understanding the impact of virtual humans’ emotions and sexual appeal on social media users’ engagement are discussed.

Modeling emotional contagion in the COVID-19 pandemic: a complex network approach

During public health crises, the investigation into the modes of public emotional contagion assumes paramount theoretical importance and has significant implications for refining epidemic strategies. Prior research predominantly emphasized the antecedents and aftermath of emotions, especially those of a negative nature. The interplay between positive and negative emotions, as well as their role in the propagation of emotional contagion, remains largely unexplored. In response to this gap, an emotional contagion model was developed, built upon the foundational model and enriched from a complex network standpoint by integrating a degradation rate index. Stability analyses of this model were subsequently conducted. Drawing inspiration from topological structural features, an enhanced model was introduced, anchored in complex network principles. This enhanced model was then experimentally assessed using Watts-Strogatz’s small-world network, Barabási-Albert’s scale-free network, and Sina Weibo network frameworks. Results revealed that the rate of infection predominantly dictates the velocity of emotional contagion. The incitement rate and purification rate determine the overarching direction of emotional contagion, whereas the degradation rate modulates the waning pace of emotions during intermediate and later stages. Furthermore, the immunity rate was observed to influence the proportion of each state at equilibrium. It was discerned that a greater number of initial emotional disseminators, combined with a larger initial contagion node degree, can amplify the emotion contagion rate across the social network, thus augmenting both the peak and overall influence of the contagion.

Image Emotion Analysis Combining Attention Mechanism and Multi-level Correlation

The development of social network has brought a large amount of image information, and the research on image emotion has gradually attracted wide attention. The current image emotion analysis methods based on multi-level features simply splice the features at each level and then classify the emotions, which not only ignores the correlation between features at different levels, but also ignores the synergistic effect between global features and local features. Therefore, this paper proposes an emotion model (MAML) based on mixed attention and multi-level dependence of images, which uses spatial and channel attention mechanisms to extract local emotion region features of images. Bi-directional Long Short Term Memory network (BiLSTM) is used to establish correlation between multi-level image global features. The experimental results of MAML model on artphoto and abstract data sets prove the validity of MAML model.

Speech Emotion Analysis of Short English Readings based on the CAM-SPAT Model

With the development of technology, voice sentiment analysis has also undergone rapid development, and its application fields are constantly expanding. Multimodal models have become a key focus of researchers due to their ability to better predict emotions. In order to help English learners improve their oral English proficiency, a deep learning based emotional analysis model for English short text reading is proposed, and this model is used to analyze emotions in English reading. Additionally, a cross-modal attention mechanism based on a prediction-assisted task was developed to identify emotions in English reading aloud in state and a two-layer attention-based bi-directional long- and short-term memory network was created to classify emotions in English reading aloud. The results of the research revealed that the classification model’s mean F1 value was 98.54%, the detection model’s mean F1 value was 85.13%, and the speech emotion analysis model’s mean F1 value was 73.55, which was not significantly different from the mean of the professionals’ ratings. The significance of the study lies in providing English learners with a method and pathway to improve their oral English proficiency.

An empirical study on learners' learning emotion and learning effect in offline learning environment.

The application of non-cognitive factors represented by facial emotion in educational evaluation has attracted much attention in recent years. There are many existing studies on facial emotion assisted education evaluation, but most of them are based on virtual learning environments, which means that the research on facial emotion and learning effect in offline learning environments is sparse. In order to solve this problem, this study designed an emotion observation experiment based on the offline learning environment, obtained the type of learner facial emotion and learning effect of 127 college students, and further explored the relationship between the two. The results show that: 1) We obtained eight types of learner emotion through the combined description method: joy, relaxation, surprise, meekness, contempt, disgust, sadness, anxiety and their respective PAD emotional mean. 2) We obtained the correlation results of the six emotions of joy, relaxation, surprise, meekness, contempt, and anxiety with the learning effect and the predicted value of the learning effect. 3) We then constructed an explanatory model of learner emotion and learning effect based on the offline learning environment.

Emotionality and performance: An emotion-regulation approach to music performance anxiety

Emotion-regulation abilities and skills are important determinants of performance success, as they lead to emotional stability and psychological well-being in the course of practicing and performing. Research on musicians’ emotionality has revealed a positive relationship between music performance anxiety (MPA) and the temperamental factors of emotional reactivity, low self-esteem, and maladaptive coping, with sex as a moderator in these relationships. In the current study, we attempted to analyze the structure of the relationships between selected variables using the assumptions of the developmental model of human emotionality as a framework for a proposed MPA regulation model. Students at music academies ( N = 196) completed the EAS Temperament Survey, the Rosenberg Self-Esteem Scale, the modified Levels of Emotional Awareness Scale, the Brief-COPE Inventory, and the revised Kenny Music Performance Anxiety Inventory. The results confirm the regularities found in the general population, female musicians showing more intense negative emotionality, temperamental activity traits, emotional awareness, and the use of emotional support strategies. In turn, male musicians obtained medium results on measures of temperamental traits, and often used problem-orientation and perspective-change strategies for emotion regulation. In general, both male and female musicians scored within the medium range for MPA. Tests of the MPA regulation model showed two pathways from temperamental traits to coping strategies: from sociability to problem orientation, and from emotionality to perspective change. There was a positive relationship between the use of the problem-orientation strategy and MPA. The major predictor negatively related to MPA was the use of the perspective-change strategy. The direction of relationships may indicate that techniques for accepting experience are more effective than control techniques for reducing MPA in the context of public performance.

English Speech Emotion Classification Based on Multi-Objective Differential Evolution

Speech signals involve speakers’ emotional states and language information, which is very important for human–computer interaction that recognizes speakers’ emotions. Feature selection is a common method for improving recognition accuracy. In this paper, we propose a multi-objective optimization method based on differential evolution (MODE-NSF) that maximizes recognition accuracy and minimizes the number of selected features (NSF). First, the Mel-frequency cepstral coefficient (MFCC) features and pitch features are extracted from speech signals. Then, the proposed algorithm implements feature selection where the NSF guides the initialization, crossover, and mutation of the algorithm. We used four English speech emotion datasets, and K-nearest neighbor (KNN) and random forest (RF) classifiers to validate the performance of the proposed algorithm. The results illustrate that MODE-NSF is superior to other multi-objective algorithms in terms of the hypervolume (HV), inverted generational distance (IGD), Pareto optimal solutions, and running time. MODE-NSF achieved an accuracy of 49% using eNTERFACE05, 53% using the Ryerson audio-visual database of emotional speech and song (RAVDESS), 76% using Surrey audio-visual expressed emotion (SAVEE) database, and 98% using the Toronto emotional speech set (TESS). MODE-NSF obtained good recognition results, which provides a basis for the establishment of emotional models.

Emotional Expression and Information Communication in English Texts Based on Artificial Intelligence Technology

Abstract This paper firstly researches English text emotion expression and information communication, classifies English text emotion expression and information communication according to the human emotion-value relationship, and summarizes the characteristics of English emotion expression and information communication. Secondly, using artificial intelligence technology, it is proposed to construct an analysis model for English text emotion and information communication using the BiLSTM neural network. To deal with the characteristics of English text quickly and efficiently, it is necessary to encode the emotional information of English text, and based on encoding, the BiLSTM neural network is applied to extract the emotional features of English text and solve the problem of the loss of emotional features through the loss function. Then, the crawler tool is used to obtain the dataset from the Chinese English module under the MOOC of Chinese universities, and the evaluation indexes are set according to the model’s performance, followed by the experimental analysis of the English text emotion expression and information conveyance. The results show that compared with the original CNN, LSTM, and T-LSTM, the BiLSTM-based neural network performs better in the task of text emotion expression and information conveyance, with the accuracy rate staying above 0.925, and the effect on the English dataset is a bit better than that on the Chinese dataset. This study aims to enhance English teaching and communication between Chinese and foreign cultures.

A broad-deep fusion network-based fuzzy emotional intention inference model for teaching validity evaluation

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students' intentions and evaluate the teaching validity through quantitative analysis in the university teaching scene. Specifically, broad-deep fusion network is carried out for facial emotion and behavior recognition. Then, fuzzy inference with personal information is used to understand the intention. The V-A emotion space is used to quantify students' studying status with originality, by mapping the output of Softmax. Finally, teaching validity is obtained by fuzzy inference. According to the recognition results, the accuracy on bimodal face and body gesture database (FABO) of our proposal is 12.21%, 1.89% and 0.78% higher than those of the sparse autoencoder-based fuzzy deep neural network (FDNNSA), geodesic flow kernel integrated with residual network (ResNet-101+GFK), the hierarchical classification fusion strategy (HCFS), respectively. Through the experiment on the self-built database, the accuracy of intention understanding is 90.48%, and the effectiveness of teaching validity evaluation method is confirmed.

Embracing the Emotion in Emotional Intelligence Measurement: Insights from Emotion Theory and Research.

Emotional intelligence (EI) has gained significant popularity as a scientific construct over the past three decades, yet its conceptualization and measurement still face limitations. Applied EI research often overlooks its components, treating it as a global characteristic, and there are few widely used performance-based tests for assessing ability EI. The present paper proposes avenues for advancing ability EI measurement by connecting the main EI components to models and theories from the emotion science literature and related fields. For emotion understanding and emotion recognition, we discuss the implications of basic emotion theory, dimensional models, and appraisal models of emotion for creating stimuli, scenarios, and response options. For the regulation and management of one's own and others' emotions, we discuss how the process model of emotion regulation and its extensions to interpersonal processes can inform the creation of situational judgment items. In addition, we emphasize the importance of incorporating context, cross-cultural variability, and attentional and motivational factors into future models and measures of ability EI. We hope this article will foster exchange among scholars in the fields of ability EI, basic emotion science, social cognition, and emotion regulation, leading to an enhanced understanding of the individual differences in successful emotional functioning and communication.

Visual Emotion Recognition Through Multimodal Cyclic-Label Dequantized Gaussian Process Latent Variable Model

A multimodal cyclic-label dequantized Gaussian process latent variable model (mCDGP) for visual emotion recognition is presented in this paper. Although the emotion is followed by various emotion models that describe cyclic interactions between them, they should be represented as precise labels respecting the emotions’ continuity. Traditional feature integration approaches, however, are incapable of reflecting circular structures to the common latent space. To address this issue, mCDGP uses the common latent space and the cyclic-label dequantization by maximizing the probability function utilizing the cyclic-label feature as one of the observed features. The likelihood maximization problem provides limits to preserve the emotions’ circular structures. Then mCDGP increases the number of dimensions of the common latent space by translating the rough label to the detailed one by label dequantization, with a focus on emotion continuity. Furthermore, label dequantization improves the ability to express label features by retaining circular structures, making accurate visual emotion recognition possible. The main contribution of this paper is the implementation of feature integration through the use of cyclic-label dequantization.

Model Of Rational And Emotional Decision Making In The First Purchase Of Cosmetics

Background: Consumers face both positive and negative impacts when buying a product for the first time. Existing studies emphasize the rational aspects in making decisions to buy cosmetics. Buying cosmetics will include liking the product (emotional aspect) and the good effects obtained from buying the product, in addition to rational considerations such as product safety (rational aspect). This study aims to determine the effect of cognitive and emotional orientation models in making decisions to buy cosmetics for the first time. Objective: This study examines the rational and emotional aspects of buying a cosmetic product for the first time. Method: This research involved 131 consumers who either bought cosmetics for the first time or a new product. Consumer age ranges were from 18 to 22 years. Respondents have used cosmetics for up to 3 years. Respondents were selected using accidental sampling. Results: showed that rational and emotional considerations significantly contribute to purchasing decisions. Consumers not only consider the benefits of these cosmetics, but also pay attention to their feelings towards these products. Conclusion: Consideration of emotional and rational aspects will influence purchasing decisions. The preferences and positive expectations consumers feel will be a more dominant factor than fear of risk. Producers need to communicate information that can make consumers happy in addition to product functions. Keywords: Ratio Considerations, Emotional Considerations, Purchasing Decision-making

The Emotional Intelligence (E.I.) and “Perrotta Human Emotions - Questionnaire - 1” (PHE-Q-1): Development, regulation and validation of a new psychometric instrument

Background: Based on the Perrotta Human Emotions Model (PHEM-2), a psychometric tool was modelled that could help the therapist gain a deep understanding of the patient's emotional capacity, and thus his emotional intelligence, also concerning follow-up and achievements during the psychotherapeutic course. Aim: To check whether the PHEM-2 can be structured into a psychometric tool that assesses the subject's emotional intelligence to calibrate the most appropriate psychotherapeutic intervention for the specific case. Methods: Clinical interview, based on narrative-anamnestic and documentary evidence, and battery of psychometric tests. Results: The PHE-Q-1 demonstrated its clinical usefulness during psychotherapy sessions to improve the patient's emotional capacity and confirmed its validity relative to statistical comparison, reporting an R = 0.999 and p ≤ 0.001. Conclusion: The validity of PHE-Q-1 in investigating the emotional component of human intelligence and its clinical utility concerning one's level of cognitive-emotional dissonance and PHEM-2, during psychotherapeutic encounters conducted according to the brief or otherwise integrated strategic approach, is confirmed.

A SENTIMENT ANALYSIS MODEL FOR ELECTROENCEPHALOGRAM SIGNALS OF STUDENTS IN UNIVERSITIES USING A CONVOLUTIONAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE MODELS

Sentiment analysis in teaching evaluation has significant implications. By analyzing students’ sentiments toward instructors, educational institutions can gain valuable insights into teaching effectiveness. These data can guide curriculum development, instructional improvements, and faculty training initiatives. Positive sentiment indicates effective teaching methods, engagement, and student satisfaction; negative sentiment flags areas that need attention. Sentiment analysis can help identify patterns, trends, and outliers, aiding in targeted interventions and personalized support. It also enables comparisons across different courses, instructors, and departments. However, it is crucial to ensure the accuracy and fairness of sentiment analysis algorithms, considering potential biases and the contextual nature of the feedback. This study proposes a sentiment classification model CNN–SVM that combines a convolutional neural network (CNN) and a support vector machine (SVM). Taking students majoring in art in comprehensive colleges and universities as the research object, by collecting the electroencephalogram (EEG) signals of students during teaching evaluation. CNN–SVM is used as the emotional analysis model to obtain the emotional analysis of teaching evaluation results. EEG is a typical physiological signal, and data based on this signal can more truly reflect student emotions. The adaptive CNN feature extraction function and the super generalization classification performance of SVM can reduce the individual differences and data noise between data, thereby improving sentiment classification performance. The experimental results demonstrate that using technology to analyze sentiment can assist educational institutions in more properly comprehending the feedback and opinions of students on instruction. With regard to sentiment analysis, the CNN–SVM method that is derived to produce the fusion algorithm has solid performance.

Enhancing Mental Health and Medication Adherence Among Men Who Have Sex With Men Recently Diagnosed With HIV With a Dialectical Behavior Therapy-Informed Intervention Incorporating mHealth, Online Skills Training, and Phone Coaching: Development Study Using Human-Centered Design Approach.

Mental health problems are common among men who have sex with men (MSM) living with HIV and may negatively affect medication adherence. Psychosocial interventions designed to address these urgent needs are scarce in China. Incorporating behavioral health theories into intervention development strengthens the effectiveness of these interventions. The absence of a robust theoretical basis for interventions may also present challenges to identify active intervention ingredients. This study aims to systematically describe the development of a mobile health-based intervention for MSM recently diagnosed with HIV in China, including the theoretical basis for the content and the considerations for its technological delivery. We used intervention mapping (IM) to guide overall intervention development, a behavioral intervention technology model for technological delivery design, and a human-centered design and cultural adaptation model for intervention tailoring throughout all steps of IM. The dialectical behavior therapy (DBT)-informed intervention, Turning to Sunshine, comprised 3 components: app-based individual skills learning, group-based skills training, and on-demand phone coaching. The theoretical basis for the intervention content is based on the DBT model of emotions, which fits our conceptualization of the intervention user's mental health needs. The intervention aims to help MSM recently diagnosed with HIV (1) survive moments of high emotional intensity and strong action urges, (2) change emotional expression to regulate emotions, and (3) reduce emotional vulnerability, as well as (4) augment community resources for mental health services. Technological delivery considerations included rationale of the medium, complexity, and esthetics of information delivery; data logs; data visualization; notifications; and passive data collection. This study laid out the steps for the development of a DBT-informed mobile health intervention that integrated app-based individual learning, group-based skills training, and phone coaching. This intervention, Turning to Sunshine, aims to improve mental health outcomes for MSM newly diagnosed with HIV in China. The IM framework informed by human-centered design principles and cultural adaptation considerations offered a systematic approach to develop the current intervention and tailor it to the target intervention users. The behavioral intervention technology model facilitated the translation of behavioral intervention strategies into technological delivery components. The systematic development and reporting of the current intervention can serve as a guide for similar intervention studies. The content of the current intervention could be adapted for a broader population with similar emotional struggles to improve their mental health outcomes.

Innovation of fresh agricultural products e-commerce operation mode based on artificial intelligence and machine learning model

Abstract In recent years, there has been an increasing interest in emotional interaction models based on artificial intelligence technology, and researchers want to deal with real-life problems in daily life through artificial intelligence and emotional interaction technology. In this paper, based on artificial intelligence and the emotional interaction model, we analyze the model architecture of emotional interaction technology and the theory related to the e-commerce operation platform. Combining the two, we propose a collaborative filtering recommendation algorithm, define system performance indexes, and recommend fresh produce to target customers with similar users’ evaluation of fresh produce through artificial intelligence technology. Using the experiments of three different recommendation systems and user behavior data of different months in the past three years as reference, it is concluded that the e-commerce operation of fresh produce based on AI and emotional interaction model can effectively improve the browsing and transaction volume of the e-commerce platform. This shows that artificial intelligence and emotional interaction model can be applied to create a new e-commerce operation mode, which provides research directions and methods for the diversified development of future e-commerce operation modes.

A Study of the Emotional Impact of Interior Lighting Color in Rural Bed and Breakfast Space Design

In architectural spaces, the ambiance created by lighting plays a significant role in influencing people’s emotions, often relying on the use of color and light. However, previous research has primarily focused on urban architectural spaces, with relatively less attention given to rural environments, especially the interior spaces of rural accommodation. The objective of this study is to investigate the impact of colored lighting within rural accommodation interiors on people’s emotions and visual perceptions. Additionally, the study aims to establish an emotional dimension model to map the experimental results onto eight basic emotions, utilizing the PAD model for quantitative analysis. To achieve this goal, the researchers recruited 90 participants and divided them into six groups, each experiencing different combinations of indoor lighting scenes with varying colors. Participants evaluated their emotions and visual perceptions. The study results indicate that the combination of cool and warm white light with colored light significantly influences the arousal level of participants but has a relatively lower impact on pleasure levels, dominance and relaxation levels. In comparing the emotions experienced after exposure to cool and warm white light followed by colored light, warm white light elicited more positive emotions. Furthermore, colored light following cool white light diminished the sense of warmth in light, while yellow–blue light enhanced visual comfort and blue–red light increased attraction. Overall, the effect of colored lighting in rural accommodation interior spaces on improving negative emotions was relatively weak. Conversely, the combination of warm white light with blue–yellow or green–yellow light had a more significant effect on enhancing visual perceptions and reducing participants’ anxiety. This research provides valuable insights and references for designing the lighting ambiance in rural accommodation interiors and lighting design, with the potential to enhance the spatial experience of rural accommodations.

Becoming a physical education teacher: Preservice teachers’ meaning-making process in the transition between a subject didactics course and the practicum

Research has shown that a critical teaching perspective is at risk if preservice teachers perceive a gap between university- and field-based courses. This study explores how preservice physical education (PE) teachers make meaning out of their participation in a subject didactics course and the practicum. Interviews conducted across 1 year revealed that preservice teachers experienced PE teaching as practical skills and the advocacy of movement and health during the university-based course but not during the practicum. In both contexts, teaching as didactic and embodied knowledge was perceived as being meaningful. The study suggests that emotional learning occasions, role models, and clear purposes are supportive for a critical teaching perspective.

FlexComb: A Facial Landmark-Based Model for Expression Combination Generation

Facial expressions are a crucial but challenging aspect of animating in-game characters. They provide vital nonverbal communication cues, but given the high complexity and variability of human faces, the task of capturing the natural diversity and affective complexity of human faces can be a labour-intensive process for animators. This motivates the need for more accurate, realistic and lightweight methods for generating emotional expressions for in-game characters. In this work, we introduce FlexComb, a Facial Landmark-based Expression Combination model, designed to generate a real-time space of realistic facial expression combinations. FlexComb leverages the highly varied CelebV-HQ dataset containing emotions in the wild, and a transformer-based architecture. The central component of the FlexComb system is an emotion recognition model that is trained on the facial dataset, and used to generate a larger dataset of tagged faces. The resulting system generates in-game facial expressions by sampling from this tagged dataset, including expressions that combine emotions in specified amounts. This allows in-game characters to take on variety of realistic facial expressions for a single emotion, which addresses this primary challenge of facial emotion modeling. FlexComb shows potential for expressive facial emotion simulation with applications that include animation, video game development, virtual reality, and human-computer interaction.

Workplace Ostracism and Instigated Workplace Incivility: A Moderated Mediation Model of Narcissism and Negative Emotions

Workplace Ostracism and Instigated Workplace Incivility: A Moderated Mediation Model of Narcissism and Negative Emotions