Emotional Model Research Articles

Sentiment Analysis in ChatGpt Interactions: Unraveling Emotional Dynamics, Model Evaluation, and User Engagement Insights

ABSTRACT The rapid evolution of AI, especially in natural language processing, has given rise to conversational AI models like ChatGPT, revolutionizing user engagement through text interactions. This study focuses on sentiment analysis within ChatGPT interactions, uncovering emotional dynamics and evaluating sentiment analysis models. Sentiment analysis holds significance in diverse domains, aiding in user sentiment interpretation. The research employs systematic methods, including data collection, analysis in the PyCharm IDE, and hypothesis testing. Findings reveal a mixed sentiment dataset, with user prompts tending to be more positive than ChatGPT responses. Sentiment during conversations exhibits dynamic shifts, and a Naive Bayes classifier-based model shows robust performance. This research enhances our understanding of sentiment analysis in ChatGPT, offering insights for refining conversational AI and user experiences in an AI-driven world.

The Role of Emotional Intelligence in Effective Leadership and Its Impact on Team Performance: A Study of the University of Ibadan, Nigeria

This study aimed to investigate the impact of emotional intelligence on leadership effectiveness in the University of Ibadan, Nigeria. The emotional intelligence model used in this study consisted of four sub-variables: self-emotion appraisal, others emotion appraisal, regulation of emotions, and use of emotions. The study found that emotional intelligence has a strong positive and significant relationship with leadership effectiveness. The results of the regression analysis showed that emotional intelligence positively relates to leadership effectiveness. The study concluded that emotional intelligence is an indicator of leadership effectiveness, and employees in the University of Ibadan, Nigeria are emotionally intelligent. Self-emotion appraisal and use of emotion were the most emotional intelligence factors impacting leadership effectiveness in the organization. On the other hand, emotion regulation seemed to be difficult to control, thus it’s the least factor in affecting leaders' effectiveness. The findings further show that use of emotion accounts for more of the variance in leadership effectiveness.

Schedule risk model of water intake tunnel construction considering mood factors and its application

The mood index r\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r$$\\end{document} was used to describe evaluator attitudes regarding the progress of a project that formed the basis of a construction period prediction model. The degrees of pessimism α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} and optimism β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document} were introduced, and an analysis model was established using α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} and β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document} to predict the construction period and completion probability Firstly, the absolute construction period of each process of tunnel No. 2 can be obtained according to the measured daily average footage of each process of tunnel No. 1. Secondly, the probability of the stoppage caused by different factors can be obtained after the statistical analysis of the factors responsible for the stoppage of tunnel No. 1. Finally, the expected construction period and completion probability of tunnel No. 2 under different pessimism and optimism conditions are obtained by using the progress risk analysis theory of emotional models and the program evaluation and review technique method. An engineering application showed that the expected construction period increased, and the completion probability decreased considerably with increasing pessimism; the opposite trend occurred as optimism increased. During the process of risk management and control, the prediction model can be used to perform precise quantitative analysis of the expected construction period and completion probability, reduce the blindness of construction management, control decisions of complex giant tunnel projects, and provide a more accurate basis for decision makers to judge risks. The findings of this study can be applied to hydraulic tunnels and can provide a reference for traffic tunnels, railway tunnels, and other similar projects.

Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review

Emotion is a subjective psychophysiological reaction coming from external stimuli which impacts every aspect of our daily lives. Due to the continuing development of non-invasive and portable sensor technologies, such as brain-computer interfaces (BCI), intellectuals from several fields have been interested in emotion recognition techniques. Human emotions can be recognised using a variety of behavioural cues, including gestures and body language, voice, and physiological markers. The first three, however, might be ineffective because people sometimes conceal their genuine emotions either intentionally or unknowingly. More precise and objective emotion recognition can be accomplished using physiological signals. Among other physiological signals, Electroencephalogram (EEG) is more responsive and sensitive to variation in affective states. Various EEG-based emotion recognition methods have recently been introduced. This study reviews EEG-based BCIs for emotion identification and gives an outline of the progress made in this field. A summary of the datasets and techniques utilised to evoke human emotions and various emotion models is also given. We discuss several EEG feature extractions, feature selection/reduction, machine learning, and deep learning algorithms in accordance with standard emotional identification process. We provide an overview of the human brain's EEG rhythms, which are closely related to emotional states. We also go over a number of EEG-based emotion identification research and compare numerous machine learning and deep learning techniques. In conclusion, this study highlights the applications, challenges and potential areas for future research in identification and classification of human emotional states.

Does it bug you eating bugs? The emotions of anthropoentomophagy

PurposeThe use of insects as food is a proposed solution for the increased demand for food worldwide, but it lacks acceptance because of restrictive emotional factors. This article aims to understand better customers' emotions’ role in considering and consuming insect-based food.Design/methodology/approachTo assess their acceptance, an experiment was developed with 38 participants living in Portugal to identify how people feel when consuming processed insect bars compared to cereal bars (of equal flavour). A video was recorded “before”, “during” and “after” the consumption of such foods, and the triggered emotions and affective states were identified using the Facial Action Coding System (FACS) and the circumplex model of affect, respectively. After consumption, the Self-Assessment Manikin (SAM) was asked to be completed.FindingsIt was observed that the valence and arousal of the emotions and affective states triggered during consumption were higher in the insect bar than in the cereal bar. Its consumption resulted in surprise and a positive evaluation. Processed insect-based foods may result in a potentially increased acceptance of this new food alternative in the market.Originality/valuePrior studies briefly identified disgust as a primary emotion activated by insect-based food. The current research deeply studied emotional responses to insect-based processed foods in the Western world using the dimensional emotional models. This study offers arguments for the insect-based food industry to invest in processed food justified by its potential for acceptance. In addition, it motivates further research focused on other insect-based products (e.g. non-processed ones).

The influence of uncertainty on indulgent consumption: a chain mediation model of curiosity and positive emotion

PurposePeople usually try to avoid uncertainty. Recently, however, uncertainty has become an emerging marketing tool in the hedonic product industry. In the case of blind box consumption, for example, the consumers become addicted to the uncertainty created by businesses, leading to repeat purchases and even indulgences. Previous research has, yet, to focus on the impact of uncertainty on indulgence and the role of emotions.Design/methodology/approachThis paper constructs and validates a chain mediation model of uncertainty triggering indulgent consumption based on the information gap theory, positive emotion theory and uncertainty resolution theory and examines the difference between resolved and unresolved uncertainty. This study also explores differences in the impact of whether uncertainty is resolved on emotions. The uncertainty-resolved group elicited a more positive emotional response than the uncertainty-unresolved group, leading to a more indulgent consumption.FindingsThe results of three studies show that uncertainty influences indulgent consumption through curiosity and positive emotion, and that curiosity and positive emotion play separate and chain mediating roles between uncertainty and indulgent consumption, respectively. We validate our central hypothesis with questionnaires among blind box consumer groups, examining the moderating role of perceived luck and risk preferences.Originality/valueThe findings shed new light on firms' use of uncertainty to promote consumer purchases.

Exploring Temporal Relationships Between Anxiety, Mood and Mental Imagery in Patients With Bipolar Disorder: A Network Analysis.

Bipolar disorder is a severe mental health problem with limited treatment success. There is a call for improving interventions, requiring an increased understanding of factors driving mood instability. One promising avenue is to study temporal associations between factors that appear relevant according to the emotional amplifier model of Holmes are changes in mood, anxiety and mental imagery. The current study used data from a recent RCT for a secondary analysis which applied a network analysis approach to explore temporal associations between weekly measurements of mania, depression, anxiety and mental imagery measured during 32 weeks in two randomised groups (N = 55) receiving either imagery-focused cognitive therapy (ImCT) or group psychoeducation (PE). Both negative intrusive mental imagery and anxiety appeared central in the network analyses, driving changes in both mania and depression, but only in the PE group. In the ImCT group, only anxiety was driving changes in mania and depression. Although exploratory, findings suggest that prior increases in anxiety and negative intrusive mental imagery might be associated with subsequent increases in depression and mania symptoms in patients with bipolar disorder. Anxiety might in turn increase negative intrusive imagery and associated negative emotions. Although more research is needed, results are in line with the emotional amplifier model and stress that future interventions with a focus on anxiety and imagery might help to improve psychosocial therapies for patients with bipolar disorder. In addition, this study suggests that a network approach is a helpful and feasible way to study mood instability, anxiety and mental imagery to increase our understanding of mechanisms underpinning mood instability.

Dynamic Emotional Transition Sampling and Emotional Guidance of Individuals Based on Conversation

Emotion is the vital path to achieving strong artificial intelligence. Therefore, it is significant to study the emotional guiding and controlling theory to enhance system intelligence. Conversational datasets in social media contain useful information, including individuals exhibiting continuously changing emotion states in response to external stimuli, and are the foundation for research on artificial emotions. We define dialog-based emotional guidance in reinforcement learning to research the emotional guidance of the discrete emotion model. This article proposes three strategies to obtain the optimal policy: 1) given the current emotional transition matrix, use the emotional Markov decision process (E-MDP) algorithm to calculate the optimal stimulus policy for each target emotion; 2) given the emotional transition sequences, use the emotional Monte Carlo algorithm to calculate the optimal stimulus policy; and 3) given the emotional transition sequences, use the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning algorithm to calculate the optimal stimulus policy. Besides, we improve the Markov chain Monte Carlo algorithm to sample emotional transition sequences and design a metric to evaluate the effectiveness of policies. Experimental results on three datasets show that these methods can more effectively guide emotion than traditional methods. Particularly, E-MDP achieves the best results while others can be more widely used in real-world scenarios.

Young and old persons' subjective feelings when facing with a non-human computer-graphics-based agent's emotional responses in consideration of differences in emotion perception

Virtual agents (computer-graphics-based agents) have been developed for many purposes such as supporting the social life, mental care, education, and entertainment of both young and old people individuals. Promoting affective communication between young/old users and agents requires clarifying subjective feelings induced by an agent's expressions. However, an emotional response model of agents to induce positive feelings has not been fully understood due to differences in emotion perception between young and old adults. We investigated subjective feelings induced when facing with a non-human computer-graphics-based agent's emotional responses in consideration of differences of emotion perception between young adults and old adults. To emphasize the differences in emotion perception, the agent's expressions were developed by adopting exaggerated human expressions. The differences in their emotion perception of happy, sadness, and angry between young and old participants were then identified through a preliminary experiment. Considering the differences in emotion perception, induced feelings when facing with the agent's expressions were analyzed from the three types of emotion sources of the participant, agent, and other, which was defined as subject and responsibility of induced emotion. The subjective feelings were evaluated using a subjective rating task with 139 young and 211 old participants. The response of the agent that most induced positive feelings was happy when participants felt happy, and that was sad when participants felt sad, regardless emotion sources in both young and old groups. The response that most induced positive feelings was sad when participants felt angry and emotion sources were participant and the agent, and that was angry when the emotion source was other. The emotion types of the response to induce most positive feelings were the same between the young and old participants, and the way to induce most positive feelings was not always to mimic the emotional expressions, which is a typical tendency of human responses. These findings suggest that a common agent response model can be developed for young and old people by combining an emotional mimicry model with a response model to induce positive feelings in users and promote natural and affective communication, considering age characteristics of emotion perception.

Bridging the gap: Using robotics to enhance emotional and social learning in K-12 education

This research explores the integration of robotics into K-12 education to enhance emotional and social learning (ESL). The theoretical framework draws from constructivism, social learning theory, experiential learning, socio-cultural theory, and emotional intelligence models. Implementation strategies include curriculum design, teacher training, student engagement, and ethical considerations. Challenges encompass ethical dilemmas, access disparities, and socio-cultural sensitivity. The future of robotics in education involves technological advances, global collaboration, and adaptive learning environments. The conclusion emphasizes the transformative potential of robotics in cultivating well-rounded individuals with technical proficiency and heightened socio-emotional skills. As the educational landscape evolves, the integration of robotics emerges as a dynamic force shaping a generation prepared for the complexities of the 21st century.

The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts

Selecting appropriate musical stimuli to induce specific emotions represents a recurring challenge in music and emotion research. Most existing stimuli have been categorized according to taxonomies derived from general emotion models (e.g., basic emotions, affective circumplex), have been rated for perceived emotions, and are rarely defined in terms of interrater agreement. To redress these limitations, we present research that served in the development of a new interactive online database, including an initial set of 364 music excerpts from three different genres (classical, pop, and hip/hop) that were rated for felt emotion using the Geneva Emotion Music Scale (GEMS), a music-specific emotion scale. The sample comprised 517 English- and German-speaking participants and each excerpt was rated by an average of 28.76 participants (SD = 7.99). Data analyses focused on research questions that are of particular relevance for musical database development, notably the number of raters required to obtain stable estimates of emotional effects of music and the adequacy of the GEMS as a tool for describing music-evoked emotions across three prominent music genres. Overall, our findings suggest that 10–20 raters are sufficient to obtain stable estimates of emotional effects of music excerpts in most cases, and that the GEMS shows promise as a valid and comprehensive annotation tool for music databases.

Acceptability Study of Emotional Expressions of In-Vehicle Virtual Assistant

Abstract We analyzed the differences in drivers’ preferences for emotional expressions from an in-vehicle virtual assistant based on data collected from 823 respondents in five countries categorized into three different cultural settings: collectivism (Korea, China and Brazil), individualism (USA) and a mixture of both (Russia). To quantify the degree of acceptance, we used a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree) on four emotion types categorized based on Thayer’s two-dimensional emotion model. Analysis revealed that respondents in all five countries preferred emotion type 1 (pleased, happy, excited) and emotion type 4 (calm, peaceful, relaxed), that is, positive emotions under Thayer’s two-dimensional emotion model expressions, and they showed low acceptance for emotion type 2 (nervous, angry, annoying) and emotion type 3 (sad, bored, sleepy), negative emotional expressions. In a cross-cultural comparative analysis, Brazil and China ranked highest in both positive emotion types (1 and 4), and Korea ranked highest in negative emotion types.

What distinguishes emotion-label words from emotion-laden words? The characterization of affective meaning from a multi-componential conception of emotions.

Past research that distinguishes between affective and neutral words has predominantly relied on two-dimensional models of emotion focused on valence and arousal. However, these two dimensions cannot differentiate between emotion-label words (e.g., fear) and emotion-laden words (e.g., death). In the current study, we aimed to determine the unique affective characteristics that differentiate emotion-label, emotion-laden, and neutral words. Therefore, apart from valence and arousal, we considered different affective features of multi-componential models of emotion: action, assessment, expression, feeling, and interoception. The study materials included 800 Spanish words (104 emotion-label words, 340 emotion-laden words, and 356 neutral words). To examine the differences between each word type, we carried out a Principal Component Analysis and a Random Forest Classifier technique. Our results indicate that these words are characterized more precisely when the two-dimensional approach is combined with multi-componential models. Specifically, our analyses revealed that feeling, interoception and valence are key features in accurately differentiating between emotion-label, emotion-laden, and neutral words.

Emotion generation method in online physical education teaching based on data mining of teacher-student interactions.

Different from conventional educational paradigms, online education lacks the direct interplay between instructors and learners, particularly in the sphere of virtual physical education. Regrettably, extant research seldom directs its focus toward the intricacies of emotional arousal within the teacher-student course dynamic. The formulation of an emotion generation model exhibits constraints necessitating refinement tailored to distinct educational cohorts, disciplines, and instructional contexts. This study proffers an emotion generation model rooted in data mining of teacher-student course interactions to refine emotional discourse and enhance learning outcomes in the realm of online physical education. This model includes techniques for data preprocessing and augmentation, a multimodal dialogue text emotion recognition model, and a topic-expanding emotional dialogue generation model based on joint decoding. The encoder assimilates the input sentence into a fixed-length vector, culminating in the final state, wherein the vector produced by the context recurrent neural network is conjoined with the preceding word's vector and employed as the decoder's input. Leveraging the long-short-term memory neural network facilitates the modeling of emotional fluctuations across multiple rounds of dialogue, thus fulfilling the mandate of emotion prediction. The evaluation of the model against the DailyDialog dataset demonstrates its superiority over the conventional end-to-end model in terms of loss and confusion values. Achieving an accuracy rate of 84.4%, the model substantiates that embedding emotional cues within dialogues augments response generation. The proposed emotion generation model augments emotional discourse and learning efficacy within online physical education, offering fresh avenues for refining and advancing emotion generation models.

Mechanisms of Emotional Experiences of Online Spectators of E-Sports Events From the Perspective of Interactive Ritual Chain

E-sports events play a crucial role as significant entertainment activities and platforms for emotional interaction among the public. This study seeks to delve into the processes underlying the emotional experiences of online spectators during interactive rituals within e-sports events. It employs the core concept of emotional energy within interaction ritual chains as framework for analysis. To accomplish this, we conducted one-on-one semi-structured interviews with 20 online spectators actively engaged in the League of Legends professional league. These interviews specifically targeted their emotions, feelings, evaluations, behaviors, and other relevant experiences both preceding, during, and subsequent to spectating. Our analysis revealed four distinct stages within the emotional energy change process: emotional arousal, immersion, climax, and continuity. These stages collectively culminate in the formulation of a dynamic, continuous, and cyclical emotional energy model. Notably, spectators adeptly employ various emotional regulation strategies to mitigate the impact of disruptive factors. The integration of ritual symbols, coupled with the positive feedback loop of emotional energy, serves to connect the five identified stages and establish a cohesive cyclic upward mechanism. These findings not only offer unique insights into the micro-level emotional experiences of online spectators but also hold practical implications for the effective management of e-sports events.

Development of an emotional response model for hospital gown design using structural equation modeling

Abstract Apparel has the potential to influence the external expression of wearer’s emotional state and can even empower them, making patients’ hospital wearing a crucial factor in their emotional experience and medical treatment. This study aims to investigate the emotional factors that drive patients’ behavioral responses to hospital gowns using the pleasure–arousal–dominance (PAD) model. With the survey conduction and data analysis, the results identified that the color and silhouette of hospital gowns lead to the emotional experience of arousal, while the structure leads to the emotional experience of dominance, which in turn brings patients a high sense of pleasure and further affect their acceptance and willingness to continue wearing hospital gowns. Based on the results of the research, new hospital gowns were designed and validated, which further confirmed the relationship between the attributes of hospital gowns and emotions of patients. Thus, by extending the PAD model to the context of patients’ use of hospital gowns, this study provides designers with a basis for creating emotionally driven atmosphere factors in the development of hospital gowns for the Chinese market that improve acceptance and continuation of hospital gowns, making a valuable contribution to knowledge in this field.

The influence of Emotional Intelligence in the choice of conflict resolution strategy

The purpose of this article is to examine the need for emotional intelligence (EI) in conflict resolution and workplace. Based on the review of emotional intelligence models, we aimed to comprehend the ability to resolve the effect of conflict and the process of regulation and processing of conflicts. Data were collected from a total of 280 university employees through Emotional Quotient Inventory (EQ-i 2.0) and Thomas-Kilmann Conflict Mode Instrument (TKI). The results showed that the categories most frequently used by the participants were 'Accommodation’, ‘Avoidance' and 'Compromise'. Women are among those who have used this kind of conflict resolution most. Additional results revealed significantly higher EI levels in females, a positive relationship between emotional intelligence and social intelligence, positive emotional intelligence in the Social Sciences department, and the significant impact of emotional intelligence on conflict resolution styles.The present article reported the impact of emotional intelligence on conflict resolution models, including the role of administrative staff in the Albanian context.

The English Reading Enjoyment Scale and its preliminary evidence of psychometric properties

ABSTRACT This study aims to fill the gaps in measuring foreign language reading enjoyment by developing an English reading enjoyment scale for Chinese university students. Scherer’s [(2000). ‘Emotions as Episodes of Subsystem Synchronization Driven by Nonlinear Appraisal Processes.’ In Emotion, Development, and Self-Organization: Dynamic Systems Approaches to Emotional Development, edited by M. D. Lewis, and I. Granic, 70–99. Cambridge: Cambridge University Press] component process model of emotion was consulted and a sequential mixed-methods approach was taken. In the qualitative part, 29 Chinese university students of English were invited to report their English reading enjoyment representations and the collected data were analyzed with a grounded theory approach, which produced 52 thematic categories. Based on the 12 mostly endorsed categories, the English Reading Enjoyment Scale was constructed and then submitted to principal components analysis and subsequently confirmatory factor analysis, leading to an 11-item scale. The internal structure and psychometric properties of the scale as well as the limitations and implications of the study are discussed.

Dialogue emotion model based on local–global context encoder and commonsense knowledge fusion attention

Dialogue emotion model based on local–global context encoder and commonsense knowledge fusion attention

Quantifying the Impact of Street Greening during Full-Leaf Seasons on Emotional Perception: Guidelines for Resident Well-Being

Quantifying the emotional impact of street greening during the full-leaf seasons in spring, summer, and fall is important for well-being-focused urban construction. Current emotional perception models usually focus on the influence of objects identified through semantic segmentation of street view images and lack explanation. Therefore, interpretability models that quantify street greening’s emotional effects are needed. This study aims to measure and explain the influence of street greening on emotions to help urban planners make decisions. This would improve the living environment, foster positive emotions, and help residents recover from negative emotions. In Hangzhou, China, we used the Baidu Map API to obtain street view images when plants were in the full-leaf state. Semantic segmentation was used to separate plant parts from street view images, enabling the calculation of the Green View Index, Plant Level Diversity, Plant Color Richness, and Tree–Sky View Factor. We created a dataset specifically designed for the purpose of emotional perception, including four distinct categories: pleasure, relaxation, boredom, and anxiety. This dataset was generated through a combination of machine learning algorithms and human evaluation. Scores range from 1 to 5, with higher values indicating stronger emotions and lower values indicating less intense ones. The random forest model and Shapley Additive Explanation (SHAP) algorithm were employed to identify the key indicators that affect emotions. Emotions were most affected by the Plant Level Diversity and Green View Index. These indicators and emotions have an intricate non-linear relationship. Specifically, a higher Green View Index (often indicating the presence of 20–35 fully grown trees within a 200 m range in street view images) and a greater Plant Level Diversity significantly promoted positive emotional responses. Our study provided local planning departments with support for well-being-focused urban planning and renewal decisions. Based on our research, we recommend the following actions: (1) increase the amount of visible green in areas with a low Green View Index; (2) plant seasonal and flowering plants like camellia, ginkgo, and goldenrain trees to enhance the diversity and colors; (3) trim plants in areas with low safety perception to improve visibility; (4) introduce evergreen plants like cinnamomum camphor, osmanthus, and pine.