Understanding Of Emotions Research Articles

Decoding viewer emotions in video ads

Understanding and predicting viewers’ emotional responses to videos has emerged as a pivotal challenge due to its multifaceted applications in video indexing, summarization, personalized content recommendation, and effective advertisement design. A major roadblock in this domain has been the lack of expansive datasets with videos paired with viewer-reported emotional annotations. We address this challenge by employing a deep learning methodology trained on a dataset derived from the application of System1’s proprietary methodologies on over 30,000 real video advertisements, each annotated by an average of 75 viewers. This equates to over 2.3 million emotional annotations across eight distinct categories: anger, contempt, disgust, fear, happiness, sadness, surprise, and neutral, coupled with the temporal onset of these emotions. Leveraging 5-second video clips, our approach aims to capture pronounced emotional responses. Our convolutional neural network, which integrates both video and audio data, predicts salient 5-second emotional clips with an average balanced accuracy of 43.6%, and shows particularly high performance for detecting happiness (55.8%) and sadness (60.2%). When applied to full advertisements, our model achieves a strong average AUC of 75% in determining emotional undertones. To facilitate further research, our trained networks are freely available upon request for research purposes. This work not only overcomes previous data limitations but also provides an accurate deep learning solution for video emotion understanding.

Update on the Neurobiology of Borderline Personality Disorder: A Review of Structural, Resting-State and Task-Based Brain Imaging Studies.

This review summarizes recent advances in research on the neurobiology of borderline personality disorder (BPD) according to structural brain imaging investigations and resting-state and task-based functional brain activation studies. Extending established findings on differences in regional brain volumes and cortical thickness between BPD and healthy controls, recent research illuminates shared and distinct brain structural characteristics compared to other psychiatric diagnoses, and uncovers relations of these brain structures with transdiagnostic symptoms and clinical features. Resting-state functional brain imaging studies reveal disruptions among adolescents and adults with BPD in frontolimbic and default-mode networks, which primarily underlie affect regulation and self-referential processes, respectively. Recent task-based functional brain imaging research builds on existing neurobiological understanding of emotion and cognition in BPD by revealing novel intersections with interpersonal- and stress-related processes. Studies of psychological and pharmacological interventions suggest possible effects on neural regions underlying emotion processing and behavioral control.

Mission FEEL! A novel emotion understanding intervention for preschoolers: A proof-of-concept study

Emotion understanding is an important competency that children begin to develop during the first years of life and serves as an essential building block for lifelong learning. Emotion understanding is linked to developmental outcomes including academic, cognitive, and social-emotional skills. Not surprisingly, efficacious interventions to promote children's social emotional skills have been developed. Scale-up of these interventions is challenging because they are time and resource intensive. Brief, fully automated interventions are promising alternatives to overcome implementation barriers. Mission FEEL! is a two-session, fully automated intervention grounded in cognitive-behavioral theory aimed at promoting emotion understanding among preschoolers. This proof-of-concept study examined the acceptability and feasibility of Mission FEEL!. We recruited 52 preschool children and their parents/guardians across Florida. Participants completed four virtual study visits: a baseline emotion understanding assessment, two intervention sessions, and a one-month follow-up emotion understanding assessment. We established a priori benchmarks to determine feasibility, acceptability, and clinical meaningfulness. The results indicated that Mission FEEL! is both feasible and acceptable. All outcomes, except two that were in the acceptable range, met the benchmarks for good or excellent. The clinical meaningfulness of the intervention was supported by parental perceptions of the program's influence on emotion-related parent-child interactions, perceived value of the program in children's daily lives, and observed difference in emotion understanding scores between baseline and follow-up. The ease of scale-up, low cost, and potential practical implications also contributed to the clinical meaningfulness. Findings from this proof-of-concept study suggest that Mission FEEL! merits advancing to the next phase of intervention development testing.

Issues in Machine Translation with reference to the Story “Thakur ka kuan” by Munshi Premchand

This paper explores the issues encountered in machine translation, specifically using Google Translate, in translating Munshi Premchand's short story Thakur Ka Kuan ("Thakur's Well"). The study identifies key challenges, including the misinterpretation of cultural references, idiomatic expressions, and contextual meaning that are integral to Premchand's narrative style. Machine translation often produces literal translations that lack the nuance and emotional depth present in the original text, leading to misrepresentation of the story’s themes. For instance, socio-cultural terms and metaphors central to the story's message are frequently mistranslated or omitted entirely. The translation also struggles with maintaining consistency in tense and sentence structure, further diluting the narrative. This paper argues that while machine translation offers convenience for basic translations, it remains insufficient for complex literary works, where cultural, emotional, and contextual understanding is crucial. A comparative analysis with a human-translated version highlights these deficiencies and suggests that human input remains essential for accurate literary translations. The findings underscore the need for improved machine learning models to address these linguistic and cultural intricacies.

Εντροπη: Shame and Identity Formation in the Letters of Ignatius of Antioch

Since the 1980s, biblical scholars have started to employ the framework of “honor and shame” to study the culture of the ancient Mediterranean region. However, this application of the social–scientific honor–shame model has led to “massive generalizations of ‘honor’ and ‘shame’”. In particular, when focusing on the concept of honor/shame rather than its lexemes, the social–scientific school ignores the nuanced nature of each Greek word group related to shame. By studying classical Greek literature, Douglas Cairns further points out a puzzling situation that the word group αἰδώς contains polarized meanings of both “to shame” and “to respect”. In this paper, we examine the puzzling double meanings of “to shame/to respect” by focusing on the use of the word group ἐντροπή in the letters of Ignatius of Antioch. This paper argues that the word group ἐντροπὴ serves a rhetorical purpose of protecting the church’s identity by rejecting beliefs/customs that are unacceptable to Christianity and promoting unity in the churches through obedience to church authorities. This paper also explores the fundamental epistemological issue of understanding emotion words in its ancient context. It provides a provisional definition for ἐντροπή, that it is a self-inhibitory emotion of sensitivity to one’s proper place in social interaction to protect one’s self-image.

Research on the Influence Mechanism of Tourists’ Pro-Environmental Behavior in Globally Important Agricultural Heritage Sites: A Case Study of the Anxi Tieguanyin Tea Culture System in China

The harmonious coexistence of humanity and nature stands as a paramount objective in the global endeavor towards ecological civilization. By vigorously promoting eco-friendly recreational activities within World Agricultural Heritage sites, we can evoke tourists’ emotional understanding and empathy towards environmental protection. This approach presents an optimal solution to the challenges faced in realizing the ecological value of these heritage sites, thereby fostering an even deeper harmony between people and nature. For this study, the Anxi Tie Guan Yin Tea Cultural System, a site of global agricultural heritage significance, was chosen as the case study. Employing structural equation modeling (SEM) analysis, a survey was administered to 452 local tourists for empirical testing. The research reveals several key findings: (1) Tourists’ environmental concern exerts a notably positive influence on their pro-environmental behaviors; (2) both nature empathy and ecological anxiety serve as mediators in the relationship between environmental concern and tourists’ pro-environmental behaviors; (3) furthermore, nature empathy and ecological anxiety exhibit a sequential, or chain, mediation effect between environmental concern and pro-environmental behaviors; and (4) lastly, the impact of nature empathy and ecological anxiety on tourists’ pro-environmental behaviors differs according to gender. Consequently, managers of agricultural heritage sites should aim to evoke tourists’ sense of nature empathy while appropriately stimulating their ecological anxiety, thereby providing guidance for ensuring a harmonious blend of human activities and the ecological environment and ultimately fostering the sustainable development of World Agricultural Heritage sites.

Leveraging Large Language Model ChatGPT for enhanced understanding of end-user emotions in social media feedbacks

For software evolution, user feedback has become a meaningful way to improve applications. Recent studies show a significant increase in analyzing end-user feedback from various social media platforms for software evolution. However, less attention has been given to the end-user feedback for low-rating software applications. Also, such approaches are developed mainly on the understanding of human annotators who might have subconsciously tried for a second guess, questioning the validity of the methods. For this purpose, we proposed an approach that analyzes end-user feedback for low-rating applications to identify the end-user opinion types associated with negative reviews (an issue or bug). Also, we utilized Generative Artificial Intelligence (AI), i.e., ChatGPT, as an annotator and negotiator when preparing a truth set for the deep learning (DL) classifiers to identify end-user emotion. For the proposed approach, we first used the ChatGPT Application Programming Interface (API) to identify negative end-user feedback by processing 71853 reviews collected from 45 apps in the Amazon store. Next, a novel grounded theory is developed by manually processing end-user negative feedback to identify frequently associated emotion types, including anger, confusion, disgust, distrust, disappointment, fear, frustration, and sadness. Next, two datasets were developed, one with human annotators using a content analysis approach and the other using ChatGPT API with the identified emotion types. Next, another round is conducted with ChatGPT to negotiate over the conflicts with the human-annotated dataset, resulting in a conflict-free emotion detection dataset. Finally, various DL classifiers, including LSTM, BILSTM, CNN, RNN, GRU, BiGRU and BiRNN, are employed to identify their efficacy in detecting end-users emotions by preprocessing the input data, applying feature engineering, balancing the data set, and then training and testing them using a cross-validation approach. We obtained an average accuracy of 94%, 94%, 93%, 92%, 91%, 91%, and 85%, with LSTM, BILSTM, RNN, CNN, GRU, BiGRU and BiRNN, respectively, showing improved results with the truth set curated with human and ChatGPT. Using ChatGPT as an annotator and negotiator can help automate and validate the annotation process, resulting in better DL performances.

«Non-conforming Pleasures»: Representations of Homosexuality in Casanova’s Histoire de Ma Vie

Giacomo Casanova’s Histoire de Ma Vie offers an interesting perspective for exploring same-sex eroticism in the eighteenth century. Despite its reputation as a classic focused on heterosexual adventures, the Memoirs reveal references to sodomy and eroticism between same-sex individuals. This article aims to identify and place in the historical context traces of this theme within the literary source. It documents biases and captures shifts in sensibilities through Casanova’s firsthand account, delving into male homoeroticism, lesbian encounters, and the evolution of Casanova’s attitude towards male sodomy at a time when terms like «homosexuality» had yet to be defined. The challenge of anachronism is addressed by acknowledging that the absence of a specific terminology in earlier periods does not negate the existence of same-sex love and intimacy. Examining the representations of same-sex eroticism in Casanova’s Histoire provides insights into the historical contextualization of male subjectivity, the understanding of emotions, and the physiological-moral discourse regarding the body-mind connection, during a pivotal turning point in the history of emotions and sexuality.

Early childhood executive function predicts concurrent and later social and behavioral outcomes: A review and meta-analysis.

Executive function (EF), the set of mental processes and skills involved in goal-oriented planning, organizing, and controlling behavior, is believed to support child development across many domains of life. However, although ample evidence suggests a relation between childhood EF and academic skills, it is less clear what its role is in domains beyond academics. We report a meta-analysis of relations between early childhood EF (assessed at 36-60 months of age) and social, health, and behavioral outcomes assessed concurrently and longitudinally (1,459 effect sizes, 158 studies, n = 144,642). No significant relations were found between EF and health outcomes. EF was concurrently associated with emotion understanding and regulation, prosocial skills, peer acceptance, and skill at lying. Both concurrently and longitudinally, EF was positively associated with peer acceptance, adaptive classroom behavior, and social competence, and negatively associated with internalizing and externalizing problems, and inattention and hyperactivity. For adolescent outcomes, only the relation between EF and internalizing could be meta-analyzed, and no significant relation was found between these variables. The magnitude of effect sizes varied, with absolute values ranging from r = .02 to .30. Moderator analyses indicated the relation between EF and behavioral outcomes varied by outcome, but there was no such finding for EF and social or health outcomes. EF was more strongly associated with teacher- versus parent-reported behavioral problems. There was little evidence that quality indicators affected effect sizes. There was also little evidence of publication bias. Overall, our results are consistent with the possibility of broad relevance of EF for child development; yet, future research should provide stronger causal tests of hypothesized relations between EF and outcomes to better understand the nature of EF and whether fostering it can promote healthy development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neural Representations of Emotions in Visual, Auditory, and Modality-Independent Regions Reflect Idiosyncratic Conceptual Knowledge.

Growing evidence suggests that conceptual knowledge influences emotion perception, yet the neural mechanisms underlying this effect are not fully understood. Recent studies have shown that brain representations of facial emotion categories in visual-perceptual areas are predicted by conceptual knowledge, but it remains to be seen if auditory regions are similarly affected. Moreover, it is not fully clear whether these conceptual influences operate at a modality-independent level. To address these questions, we conducted a functional magnetic resonance imaging study presenting participants with both facial and vocal emotional stimuli. This dual-modality approach allowed us to investigate effects on both modality-specific and modality-independent brain regions. Using univariate and representational similarity analyses, we found that brain representations in both visual (middle and lateral occipital cortices) and auditory (superior temporal gyrus) regions were predicted by conceptual understanding of emotions for faces and voices, respectively. Additionally, we discovered that conceptual knowledge also influenced supra-modal representations in the superior temporal sulcus. Dynamic causal modeling revealed a brain network showing both bottom-up and top-down flows, suggesting a complex interplay of modality-specific and modality-independent regions in emotional processing. These findings collectively indicate that the neural representations of emotions in both sensory-perceptual and modality-independent regions are likely shaped by each individual's conceptual knowledge.

Perceptions and impacts of the Happy Program on socio-emotional competencies: a mixed-methods study in primary education

Introduction: The "Happy Program" implemented by AMCO across various schools in Latin America has been the subject of a mixed-methods study assessing its impact on the socio-emotional competencies of primary school students. The study focuses on the perceptions of both teachers and students, integrating quantitative and qualitative techniques for a comprehensive evaluation. Methods: Mixed-methods design to thoroughly assess the impacts of the Happy Program on socio-emotional competencies among primary school students. This design incorporates both quantitative and qualitative approaches to provide a multi-dimensional understanding of the program’s effects. Teacher’s study, this segment involves a survey distributed to 57 teachers, designed to capture their perceptions before and after the implementation of the Happy Program. The survey measures changes in their views on students' socio-emotional skills and the overall classroom environment. Teachers are asked to rate the program’s influence on student behavior and emotional management, and to comment on areas for improvement, such as activity variety and family engagement. Parallel to the teacher survey, a larger scale study surveys 1,526 students. This study aims to gather direct feedback from the students about their experiences with the Happy Program. Students respond to questions about their emotional understanding, how they manage emotions, and their interactions with peers and family members as influenced by the program. This data is crucial for measuring the perceived effectiveness of the program from the students’ perspective. Findings/Results: Teachers reported a generally positive impact of the program on students, noting enhancements in key skills such as self-regulation and empathy. However, they also identified areas for improvement, such as activity diversification and greater family engagement. The results detail an increase in emotional understanding and management skills among students, with 76% of teachers observing significant improvements in these areas following the program's implementation.

Sentiment Analysis and Facial Expression Recognition in Customer Service Interactions

In the evolving landscape of digital customer service, the need for advanced methods to accurately understand and respond to customer emotions has become critical. Traditional systems often rely solely on textual data, missing non-verbal cues that significantly contribute to the customer's emotional state. This study proposes a combined approach integrating Facial Expression Recognition (FER) and Natural Language Processing (NLP) to enhance emotion detection accuracy in customer service interactions. The FER component employs Convolutional Neural Networks (CNNs) to analyze facial expressions, while the NLP component uses Long Short-Term Memory (LSTM) networks to process textual data. This multimodal system aims to provide a comprehensive understanding of customer emotions by capturing both verbal and non-verbal cues. Experiments demonstrate that the integrated FER and NLP model significantly outperforms standalone models, achieving an accuracy of 92.3%, compared to 85.2% for FER-only and 87.4% for NLP-only models. The results highlight the benefits of a multimodal approach, showing substantial improvements in both training and validation performance. This study also compares the proposed model with other state-of-the-art models such as the Deep Learning Assisted Semantic Text Analysis (DLSTA) and Multimodal Emotion Recognition using Deep Belief Networks (DBN). While DLSTA achieves higher accuracy in text-based emotion detection, and DBNs provide robust emotion classification by integrating various modalities, our model effectively balances the strengths of both visual and textual data. The findings suggest that integrating FER and NLP can significantly enhance the quality of customer service by enabling more empathetic and effective interactions. Future work will focus on optimizing computational efficiency, addressing data variability, and ensuring adaptability across diverse customer service scenarios.

UNDERSTANDING ALEXITHYMIA: A PSYCHOSOCIAL PERSPECTIVE

Objective: The objective of the review is to understand multifaceted construct of alexithymia and its association with psychosocial variables. Method: A thorough examination of the literature on the construct of alexithymia was conducted, encompassing studies published over several years. The empirical findings over the years regarding various psychosocial variables, including attachment styles and empathy were also reviewed. The Sociodemographic characteristics and comorbidity of alexithymia with other mental health conditions were also analyzed. Results: The review revealed that attachment bond plays a significant role in the development of alexithymia, childhood trauma due to unhealthy mother-infant bond can be one of the reasons for difficulty in understanding emotions which leads to difficulties in empathy as well. Furthermore, alexithymia has been found to be comorbid with depression, anxiety, autism and other mental illnesses. Conclusion: The findings of the review suggests that alexithymia is an important construct which can influence various psychosocial aspects of life. Although some of the variables have been extensively studied, much remains to be unclear. Further research will add up value to comprehensively evaluate the impact of alexithymia on psychosocial aspects Keywords: alexithymia, psychosocial perspective, attachment styles, empathy.

Age-induced changes in affective prosody comprehension and its relationship with general cognitive ability and social support utilization among older adults

ABSTRACT Aging can impact emotional recognition, affecting older adults’ mental health and social function. This study examined how aging affects affective prosody comprehension (APC: understanding emotions through speech) across seven emotions (happiness, surprise, sadness, anger, fear, disgust, and neutrality) and its relationship with cognitive function (via the Montreal Cognitive Assessment) and social support (via the Social Support Rating Scale) in 199 cognitively normal older adults. We found that older adults had lower APC accuracy and more errors, often mistaking negative emotions for neutral or positive ones. APC accuracy was significantly associated with social support, and a partial least squares (PLS) cognitive component fully mediated the relationship between the APC component and social support utilization, explaining 61.7% of the total effect. These results suggest that declines in APC during aging are linked to social support utilization through cognitive function, offering insights for interventions to improve social and cognitive health in older adults.

Expressivity attributed to music affects the smoothness of bowing movements in violinists.

Playing music is a complex task that relies on the combination of musicians' technical and expressive skills. While the literature has investigated the effects of musical expressivity on the listeners, the way how technical difficulty and emotional expressivity affect musicians during playing has surprisingly received no attention. In an attempt to fill this gap in the literature, we collected behavioral and physiological data from twelve violinists playing 29 pieces that included both technical exercises and excerpts from classical repertoire for violin. After playing each stimulus, participants rated it for emotional expressivity and technical difficulty. During the entire session, cardiac parameters and electrodermal activity were collected, together with the kinematic parameters of the bowing gesture. A set of Linear Mixed-Effect (LME) models suggested that emotional expressivity attributed to music affected the fluidity of bowing (i.e., smoothness), with the excerpts rated as more expressive being performed in a less smooth way. In contrast, LME models revealed no effects of expressivity and technical difficulty on any of the physiological parameters of violinists. Our results offer novel insights into the psychophysiological dynamics that link motor parameters with musical expressivity. These findings could influence educational practices in music and deepen our understanding of aesthetic emotions.

The Impact of Cues on Joint Attention in Children with Autism Spectrum Disorder: An Eye-Tracking Study in Virtual Games.

Joint attention (JA), a core deficit in children with autism spectrum disorder (ASD), is crucial for social interaction, emotional understanding, and cognitive development. This study aims to compare and analyze the eye-tracking data of ASD and typically developing children (TDC) during virtual games, exploring how different cue types affect JA performance in ASD children. A total of 31 TDC and 40 ASD children participated in the study. Using eye-tracking devices, we recorded the children's eye movements as they played virtual games, selecting the correct target based on cues provided by virtual characters. Our findings revealed that different cue types significantly impacted the game scores of ASD children but had no significant effect on TDC, highlighting a notable disparity between the two groups. ASD children showed a lower fixation frequency, irregular fixation paths, and increased attention to non-target objects compared to TDC. Interestingly, among the three cue types, ASD children exhibited a preference for the third type, leading to longer fixation on the region of interest and higher game scores. These results underscore the importance of cue selection in enhancing JA in ASD children. This study provides novel insights into the JA deficits in ASD children and offers a scientific basis for the development of targeted and individualized intervention programs.

The impact and application of artificial intelligence technology on mental health counseling services for college students

With the intensification of social pressure and the enhancement of mental health awareness, the mental health issues of college students have become increasingly prominent, attracting social attention. Mental health counseling services, as an important way to alleviate students’ psychological stress, are facing the dual challenges of a shortage of professionals and growing service demands. In recent years, the application of artificial intelligence (AI) technology in the field of mental health has gradually risen, and its advantages in data analysis, pattern recognition, and natural language processing provide new solutions for mental health counseling services. However, existing research still faces problems such as insufficient understanding and limited emotional interaction capabilities in practical applications. This paper delves into the application of AI technology in mental health counseling services for college students and innovates and improves upon the deficiencies in existing research. The study focuses on two main areas: First, word vector generation technologies based on statistics and language models are used according to different application scenarios, and their effectiveness in the analysis of mental health counseling texts is compared. Second, an improved Seq2Seq model is proposed to enhance the emotional understanding and interaction capabilities of emotional dialogue generation algorithms in mental health counseling. This study not only provides technological support for college mental health counseling services but also opens new research directions and perspectives for the application of AI in the field of mental health.

Emotion understanding as third-person appraisals: Integrating appraisal theories with developmental theories of emotion.

Emotion understanding as third-person appraisals: Integrating appraisal theories with developmental theories of emotion.

Correlation mining of multimodal features based on higher-order partial least squares for emotion recognition in conversations

In fields requiring an understanding of emotions, such as digital human interaction and public opinion analysis, achieving a dependable and interpretable model for mining correlations among multimodal features remains a primary objective. However, current deep learning methods often lack transparency and suffer from low interpretability. To address these challenges, we propose a novel Correlation Mining method based on Higher-Order Partial Least Squares (HOPLS) for multimodal Emotion Recognition in conversations (CMHER) in this paper. CMHER innovatively combines HOPLS with transformers and Gated Recurrent Units (GRUs) to compute correlation matrices within unimodal data streams and between cross-modal sources. HOPLS projects source data into a latent space to predict target data via correlation matrix computations, eliminating the need for Graphical Processing Unit (GPU) acceleration and making it suitable for experimental and edge systems. The integration of HOPLS with deep neural networks involves preprocessing multimodal features into suitable dimensions and latent representations, followed by HOPLS computing correlation matrices for cross-modal latent vectors and final labels through optimal joint subspace approximation, which aims at the improvements of both interpretability and reliability. Additionally, a generalization error fitting module further refines the predicted correlation matrices to improve predictive capability and overall model performance. Experiments on two public datasets validate the superiority of our proposed method.

Neurosciences and Emotional States of Latin American University Professors in the Post-Covid-19 Pandemic Stage

The article aimed to determine the relationship between neuroscience and emotional states in university faculty members from Latin America during the post-pandemic stage of COVID-19. It highlights the importance of understanding emotions from a neuroscientific perspective to develop strategies that enhance emotional management and resilience in the educational context. The descriptive-correlational research included a sample of 318 faculty members from various Latin American countries. The results revealed severe levels of stress (88.4%) and anxiety (92.1%), as well as inadequate (40.9%) or moderate (46.9%) levels of resilience among the faculty members. A strong positive correlation was found between neuroscience and emotional states (rho=0.860), as well as a good positive correlation between neuroscience and resilience (rho=0.739). The study concludes that the knowledge and application of neuroscience principles could contribute to lower levels of stress and anxiety and increase resilience in university faculty members, emphasizing the importance of integrating these disciplines into the understanding and management of emotions in the educational field.