Distinct Emotions Research Articles

In defence of taking offence: a reply to critics

ABSTRACT This article replies to the insightful contributions to the book symposium for On Taking Offence, These range from theoretical questions about how we should conceptualise an emotion like offence and the role of empirical evidence when justifying it, to practical questions about who has the power to take offence effectively and how to dispute another's offence-taking. In this reply, I first defend offence as a distinct emotion. Second, I argue against the implicit conception of social standing that underpins some of these challenges, as static and fixed rather than dynamic, emerging from the particularities of particular social interactions, and easily threatened. Third, I address the instrumental justification of offence. I conclude with some unanswered questions, and some reasons to remain optimistic about what taking offence can do.

Playing away from home: a qualitative sentiment analysis of online viewers’ non-social emotions on sex tourism vlogs

ABSTRACT Popular yet so controversial, sex tourism remains a significant attraction in Southeast Asia, generating an abundance of scholarly explorations. However, little is known about the emotions expressed by viewers on user-generated content regarding this contentious form of tourism. Thus, this qualitative sentiment analysis, which aims to dilate non-social emotions found in comments expressed by online viewers on video content featuring sex tourism in Southeast Asia, is conducted. Through Data Miner, online comments (n = 10,864) were gathered and extracted from purposively selected sex tourism vlogs on YouTube, which were then subjected to Phronetic Iterative Qualitative Data Analysis (PIQDA) (Tracy, 2013). A member-checking procedure was performed further to establish the validity of the study’s findings. Interestingly, a model dubbed YouTube online viewers’ non-social emotions on sex tourism as a magnetic field consisting of labelled emotions known as enabling and disabling has emerged in this qualitative study. Notably, enabling emotions capture distinct emotions of interest, admiration, joy, amusement, satisfaction, sexual desire, excitement, awe, and romance. The second notion, disabling emotions, encapsulates patent emotions of dislike, anger, boredom, sadness, confusion, emphatic pain, disappointment, disgust, fear, anxiety, and awkwardness. Theoretical and practical implications, limitations, and recommendations on non-social emotions in tourism were discussed in this paper.

EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts

Understanding emotional states is pivotal for the development of next-generation human-machine interfaces. Human behaviors in social interactions have resulted in psycho-physiological processes influenced by perceptual inputs. Therefore, efforts to comprehend brain functions and human behavior could potentially catalyze the development of AI models with human-like attributes. In this study, we introduce a multimodal emotion dataset comprising data from 30-channel electroencephalography (EEG), audio, and video recordings from 42 participants. Each participant engaged in a cue-based conversation scenario, eliciting five distinct emotions: neutral, anger, happiness, sadness, and calmness. Throughout the experiment, each participant contributed 200 interactions, which encompassed both listening and speaking. This resulted in a cumulative total of 8,400 interactions across all participants. We evaluated the baseline performance of emotion recognition for each modality using established deep neural network (DNN) methods. The Emotion in EEG-Audio-Visual (EAV) dataset represents the first public dataset to incorporate three primary modalities for emotion recognition within a conversational context. We anticipate that this dataset will make significant contributions to the modeling of the human emotional process, encompassing both fundamental neuroscience and machine learning viewpoints.

Implementing social and affective touch to enhance user experience in human-robot interaction.

In this paper, we discuss the potential contribution of affective touch to the user experience and robot performance in human-robot interaction, with an in-depth look into upper-limb prosthesis use as a well-suited example. Research on providing haptic feedback in human-robot interaction has worked to relay discriminative information during functional activities of daily living, like grasping a cup of tea. However, this approach neglects to recognize the affective information our bodies give and receive during social activities of daily living, like shaking hands. The discussion covers the emotional dimensions of affective touch and its role in conveying distinct emotions. In this work, we provide a human needs-centered approach to human-robot interaction design and argue for an equal emphasis to be placed on providing affective haptic feedback channels to meet the social tactile needs and interactions of human agents. We suggest incorporating affective touch to enhance user experience when interacting with and through semi-autonomous systems such as prosthetic limbs, particularly in fostering trust. Real-time analysis of trust as a dynamic phenomenon can pave the way towards adaptive shared autonomy strategies and consequently enhance the acceptance of prosthetic limbs. Here we highlight certain feasibility considerations, emphasizing practical designs and multi-sensory approaches for the effective implementation of affective touch interfaces.

Advanced differential evolution for gender-aware English speech emotion recognition

Speech emotion recognition (SER) technology involves feature extraction and prediction models. However, recognition efficiency tends to decrease because of gender differences and the large number of extracted features. Consequently, this paper introduces a SER system based on gender. First, gender and emotion features are extracted from speech signals to develop gender recognition and emotion classification models. Second, according to gender differences, distinct emotion recognition models are established for male and female speakers. The gender of speakers is determined before executing the corresponding emotion model. Third, the accuracy of these emotion models is enhanced by utilizing an advanced differential evolution algorithm (ADE) to select optimal features. ADE incorporates new difference vectors, mutation operators, and position learning, which effectively balance global and local searches. A new position repairing method is proposed to address gender differences. Finally, experiments on four English datasets demonstrate that ADE is superior to comparison algorithms in recognition accuracy, recall, precision, F1-score, the number of used features and execution time. The findings highlight the significance of gender in refining emotion models, while mel-frequency cepstral coefficients are important factors in gender differences.

Perspectives on sperm donor anonymity: insights from donor-conceived adults in Belgium.

Are donor-conceived adults in Belgium interested in obtaining donor information, and do these interests vary based on their family backgrounds? Donor-conceived adults express a significant interest in obtaining donor-related information, with the highest interest reported by offspring from heterosexual couples compared to those from lesbian couple-parented or single-parent families. In Belgium, sperm donation is mainly anonymous, but the rise of direct-to-consumer genetic testing challenges this anonymity. This was a cross-sectional study involving an online nationwide survey conducted from July 2022 to October 2023. Participants, aged 18years and older and being aware of their anonymous sperm donor-conceived status, were recruited through various channels. A total of 203 participants were included: 62.6% grew up in heterosexual families with infertile fathers, 26.1% with lesbian couples, 8.4% with single parents, and 3.0% in various or diverse family structures. The survey was available in both French and Dutch and consisted of 43 questions, including a mix of yes/no questions and multiple-choice items. The average age of disclosure was 16.5 years, with notably later disclosure in heterosexual couple-parented households. A substantial 82.8% of donor-conceived individuals expressed a keen interest in obtaining non-personally identifiable donor information, while 69% were curious about personally identifiable donor data. Furthermore, 61.6% conveyed a desire for personal contact with their donors, and 26.6% advocated for the inclusion of the donor's name on their birth certificates. Participants raised in lesbian two-parent families exhibited the lowest level of interest in donor-related information compared with those raised in other family structures. An overwhelming 90.1% wondered about the possibility of having half-siblings from the same sperm donor. Analysis of survey responses on DNA database registration revealed that 55.2% of donor-conceived offspring were already registered, with 68.8% discovering the same donor offspring and 30.4% successfully locating their donors. Compared to individuals from other family structures, those raised in heterosexual couple-parented households exhibit a less positive attitude toward their conception through anonymous sperm donation. About 61.6% of donor-conceived individuals reported experiencing distinct emotions compared to their peers, while 44.1% encountered psychological difficulties related to anonymous sperm donation, primarily attributed to late disclosure. The majority supported the idea of informing the donor about the number of children he facilitated to conceive. Lastly, the study highlighted that 21.2% of donor-conceived adults considered becoming donors themselves, and 31.3% expressed willingness to use an anonymous donor whenever faced with fertility challenges. Our sample size may not fully represent all adults conceived through anonymous sperm donation in Belgium. Participation bias may have influenced the results, especially due to the overrepresentation of participants from heterosexual couples. Additionally, an association exists between individuals raised by heterosexual couples and late disclosure, complicating the analysis by introducing a confounding factor. The findings of this study contribute to a better understanding of the needs and preferences of donor-conceived adults, with significant potential impact on patient education and healthcare policy. Study funding was not obtained for this research. There are no conflicts of interest to disclose. N/A.

Consumer coping via fashion shopping during the COVID-19 pandemic: a Stimulus-Organism-Response (SOR) approach

ABSTRACT This study aims to understand consumers’ coping strategies associated with fashion shopping behaviours during the COVID-19 pandemic through the lens of the Stimulus-Organism-Response (SOR) framework. Specifically, the impacts of external stimuli (perceived risk and perceived uncertainty) on consumers’ internal affective states (fear, anxiety, and hope), and the subsequent behavioural coping responses, including emotion-focused and problem-focused coping reflected through fashion consumption were investigated. Additionally, the moderating effect of self-efficacy was tested. Data were collected via an online survey from 815 U.S. consumers and analyzed using structural equation modelling. The results showed that consumers’ cognitive appraisals on risks and uncertainty of the COVID-19 pandemic induced distinct emotions, which had heterogeneous effects on consumers’ intention to engage in either emotion-focused or problem focused coping behaviours. This study provides a foundation for understanding evolving patterns of fashion shopping behaviours when coping with the stress associated with the pandemic.

IMPROVING E-LEARNING BY FACIAL EXPRESSION ANALYSIS

Modern technology has become a vital part of our daily lives, and the world has undergone remarkable advancements in various scientific and technological fields. The advancement of technology presents a variety of opportunities for students to promote academic development and make it easier to access education through online learning systems. The most difficult and most demanding task during learning is to be aware of and support the emotional side of students. Recognizing one's emotions is easy for humans, but it is a challenging task for computers due to the specific features of the human face. However, recent advances in computing and image processing have made it possible and easy to detect and categorize emotions in images and videos. This paper focuses on detecting learners' emotions in real time during synchronous learning. In this regard, a video/chat application has been developed for the tutor to detect the emotions of the learners while presenting his lesson. The emotions detected are separated into three states (Satisfied, Neutral and Unsatisfied); each state is made up of two or three distinct emotions. The objective is to assist teachers in adapting teaching methods in virtual learning settings according to the emotions of learners.

The meaning of touch: Relational and individual variables shape emotions and intentions associated with imagined social touch.

Touch is a key channel for conveying meaning in social interactions. The affective quality of touch and its effects on well-being are shaped by relational context (relationship between touch giver vs. recipient) and person variables (e.g. adult attachment style). Yet, such effects have not been explored in relation to the meaning ascribed to touch. We used data from the Touch Test, the world's largest touch survey, which included questions on the degree to which people felt and related specific emotions and intentions to imagined gentle stroking touch and hugs. In N = 23,428, we examined how relational context (imagined source of touch) and person variables (gender, recalled positive childhood touch and adult attachment style) were associated with positive (e.g. love, desire, support) and negative (e.g. fear, anger, warning) emotions and intentions related to imagined touch. Love, desire and support were endorsed more when participants had had their partner (vs. someone else) in mind, and women (vs. men) gave lower ratings for desire overall. Gentle stroking touch was most linked with arousal when participants had had their partner in mind. Further, more positive childhood touch and secure and anxious attachment scores were associated with more positive emotions and intentions, while the opposite was found for avoidant attachment scores. Lastly, positive childhood touch and higher anxious attachment scores were related to greater discrimination between distinct emotion and intention categories, while higher attachment avoidance was associated with reduced discriminability. Thus, contextual and person variables matter in shaping the meaning of social touch.

Women’s emotional roller coasters during pregnancy as a consequence of infertility: a qualitative phenomenological study

Women experiencing infertility tend to experience distinct emotions during pregnancy following infertility. Individuals in this population often struggle with psychological and social challenges during pregnancy. This qualitative phenomenological study determines how infertility experiences affect mothers’ mental well-being during pregnancy as well as their experiences and emotions after becoming pregnant. A face-to-face, in-depth interview with 15 women with infertility was conducted between November 2022 and May 2023. The study adhered to ethical guidelines, with informed written consent obtained prior to interviews and voice recordings. The COREQ checklist is applied to follow the EQUATOR guidelines for reporting research and the data were coded using MAXQDA.20 software. A thematic analysis revealed four main themes and 15 codes. Main themes included “Over Emotional Burden,” “Overprotection,” “Overthinking,” and “Social Activity Changes.” Pregnancy after infertility is a unique and emotionally charged experience for women, encompassing a spectrum of feelings that can be difficult to express. The importance of caring for these women and receiving support from their partners, families, and healthcare providers should not be overstated. Healthcare providers should be aware of these emotional challenges so they can provide better support and counselling to improve women’s overall pregnancy experience. It is believed that empathetic communication and tailored support can significantly improve the psychological well-being of this population. Psychologists, psychiatrists, and midwives should also be more attentive to mothers’ emotional challenges and integrate comprehensive emotional support and provide coping mechanisms in perinatal care programs.

EmoBone: A Multinational Audio Dataset of Emotional Bone Conducted Speech

AbstractThis paper introduces EmoBone, a comprehensive audio‐only emotional bone‐conducted speech dataset featuring speakers from various countries. The dataset comprises speeches from 28 individuals representing 10 different nations, with each participant delivering 10 sentences designed to evoke distinct emotions. In addition to an air‐conducted microphone, the recordings utilized bone conduction technology, transmitting sound directly to the speakers' inner ears, ensuring high‐quality emotional speech recordings. To assess the validity of the dataset, 80 university students from Bangladesh listened to the recordings and successfully identified the expressed emotions with an accuracy exceeding 76%. Statistical methods were also employed to evaluate the reliability of the dataset, revealing a high level of agreement among raters. EmoBone, with a cumulative duration surpassing 19 h and 15 680 unique utterances, stands as the most extensive emotional speech dataset available. This makes it a valuable tool for studying how emotional speech varies across cultures. Furthermore, due to its utilization of bone conduction technology, EmoBone facilitates the study of acoustic features in emotional speech from diverse dimensions. The data that supports the findings of this study is available upon reasonable request. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Recently, the analysis of emotions in social media has been considered a significant NLP task in digital and social-media-driven environments due to their pervasive influence on communication, culture, and consumer behavior. In particular, the task of Aspect-Based Emotion Analysis (ABEA), which involves analyzing the emotions of various targets within a single sentence, has drawn attention to understanding complex and sophisticated human language. However, ABEA is a challenging task in languages with limited data and complex linguistic properties, such as Korean, which follows spiral thought patterns and has agglutinative characteristics. Therefore, we propose a Korean Target-Attention-Based Emotion Classifier (KOTAC) designed to utilize target information by unveiling emotions buried within intricate Korean language patterns. In the experiment section, we compare various methods of utilizing and representing vectors of target information for the attention mechanism. Specifically, our final model, KOTAC, shows a performance enhancement on the MTME (Multiple Targets Multiple Emotions) samples, which include multiple targets and distinct emotions within a single sentence, achieving a 0.72% increase in F1 score over a baseline model without effective target utilization. This research contributes to the development of Korean language models that better reflect syntactic features by innovating methods to not only obtain but also utilize target-focused representations.

Measuring emotional contagion as a multidimensional construct: the development and initial validation of the contagion of affective phenomena scales

ABSTRACT We offer an alternative conceptualization of the construct of susceptibility to emotional contagion and four related studies where two separate measures were developed and initially validated. The Contagion of Affective Phenomena Scale-General (CAPS-G) is a 5-item scale that measures the general susceptibility to the contagion of affect, and the Contagion of Affective Phenomena Scale – Emotion (CAPS-E) assesses six distinct emotions. Study 1 generated items with experts. Study 2 explored and confirmed construct validity and the factorial structure of both measures using exploratory structural equation modeling. Study 3 established test–retest reliability, concurrent validity, and discriminant validity. Study 4 found predictive validity with a sample of competitive swimmers. In four separate samples, a 21-item and 6-factor first-order structure of CAPS-E provided the best model fit. We provide initial evidence that supports the use of CAPS-E and CAPS-G as reliable and valid measures of the susceptibility to contagion of affective phenomena.

Depression Analysis Using Face Recognition System

Stress is a part of life it is an unpleasant state of emotional arousal that people experience in situations like working for long hours in front of a computer. Computers have become a way of life, much life is spent on computers and hence we are therefore more affected by the ups and downs that they cause us. One cannot just completely avoid their work on computers but one can at least control his/her usage when being alarmed about him being stressed at a certain point of time. Monitoring the emotional status of a person who is working in front of a computer for a longer duration is crucial for the safety of a person. In this work, a real-time non-intrusive videos are captured, which detects the emotional status of a person by analyzing the facial expression. In each frame of the video, we identify a distinct emotion and assess stress levels over consecutive hours of captured footage. Our approach involves employing a methodology enabling model training and analysis of predictive variances. Utilizing Theano, a Python framework, we enhance both execution and development times for the linear regression model, serving as the deep learning algorithm in this context. Experimental findings indicate that our system effectively performs across diverse age groups with a generic model.

Development and validation of the Emotional Climate Change Stories (ECCS) stimuli set

Climate change is widely recognised as an urgent issue, and the number of people concerned about it is increasing. While emotions are among the strongest predictors of behaviour change in the face of climate change, researchers have only recently begun to investigate this topic experimentally. This may be due to the lack of standardised, validated stimuli that would make studying such a topic in experimental settings possible. Here, we introduce a novel Emotional Climate Change Stories (ECCS) stimuli set. ECCS consists of 180 realistic short stories about climate change, designed to evoke five distinct emotions—anger, anxiety, compassion, guilt and hope—in addition to neutral stories. The stories were created based on qualitative data collected in two independent studies: one conducted among individuals highly concerned about climate change, and another one conducted in the general population. The stories were rated on the scales of valence, arousal, anger, anxiety, compassion, guilt and hope in the course of three independent studies. First, we explored the underlying structure of ratings (Study 1; n = 601). Then we investigated the replicability (Study 2; n = 307) and cross-cultural validity (Study 3; n = 346) of ECCS. The collected ratings were highly consistent across the studies. Furthermore, we found that the level of climate change concern explained the intensity of elicited emotions. The ECCS dataset is available in Polish, Norwegian and English and can be employed for experimental research on climate communication, environmental attitudes, climate action-taking, or mental health and wellbeing.

Transforming Customer Digital Footprints into Decision Enablers in Hospitality

The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making.

Emotion malleability beliefs matter in emotion regulation: a comprehensive review and meta-analysis

ABSTRACT Individuals’ beliefs about the malleability of emotions have been theorised to play a role in their psychological distress by influencing emotion regulation processes, such as the use of emotion regulation strategies. We conducted a meta-analysis to test this idea across studies with a focus on the relationships between emotion malleability beliefs and five distinct emotion regulation strategies: cognitive reappraisal, suppression, avoidance, rumination, and acceptance. Further, using two-stage meta-analytic structural equation modelling (TSSEM), we examined whether the emotion regulation strategies mediate the cross-sectional relationship between emotion malleability beliefs and psychological distress across studies. Thirty-seven studies were included in the meta-analyses and 55 cross-sectional studies were included in the TSSEM. Results demonstrated that, across studies, emotion malleability beliefs were significantly associated with greater use of putatively helpful strategies (particularly with cognitive reappraisal) and less use of putatively unhelpful strategies (particularly with avoidance). The use of cognitive reappraisal and avoidance partially mediated the relationship between emotion malleability beliefs and psychological distress. These results highlight the importance of considering beliefs about the malleability of emotions in the context of emotion regulation. These findings suggest the potential role of emotion malleability beliefs in interventions for individuals with emotion regulation-related difficulties and psychological distress.

A novel concatenated 1D-CNN model for speech emotion recognition

Speech Emotion Recognition (SER) has conventionally been executed solely through acoustic data. Convolutional Neural Network (CNN) in Deep Learning (DL) is a cutting-edge technique that has been effectively used in diverse fields, such as analyzing speech emotion. This study utilizes the Mel Frequency Magnitude Coefficient (MFMC), a modified variant of the Mel Frequency Cepstral Coefficient (MFCC) feature, in conjunction with a one-dimensional (1D) CNN to increase the SER performance. The accuracy of SER is evaluated utilizing the proposed models. Model 1 refers to MFCC-1D-CNN, model 2 refers to MFMC-1D-CNN, and model 3 refers to the concatenated model of model 1 and model 2. The models were evaluated on four datasets comprising eight distinct emotions, specifically anger, happiness, sadness, boredom, fear, surprise, disgust, neutral, and calm. The proposed models, namely model 1, model 2, and model 3, were evaluated on the four datasets using coefficients of 12, 24, and 30. The Proposed Model 1 achieved accuracy rates for EMO-DB, EMOVO, SAVEE, and RAVDESS of 88.1%, 87.5%, 75%, and 97.7% for the respective coefficients. While the proposed model 2 achieved accuracy rates of 93.8%, 98.3%, 90.3%, and 97.5% for the same coefficients. However, the proposed concatenated model 3 outperformed the accuracy of 95.6%, 99.4%, 91.7%, and 98.1% for the four datasets EMO-DB, EMOVO, SAVEE, and RAVDESS compared to model 1 and model 2. Experimental results indicate that the proposed concatenated model 3 improves accuracy and prevents the model from overfitting.

Fuelling contempt for police: explaining why procedural injustice and police ineffectiveness damage Muslims’ trust in police

ABSTRACT Research shows that discriminatory policing can erode public trust in police. Yet, little research has examined the role that contempt can play in explaining this relationship. Focusing on Muslims in Australia, we test whether procedural justice and police effectiveness are associated with enhanced trust in police across two policing contexts (general duties policing and counterterrorism policing), and whether Muslims’ level of contempt for police mediates these relationships. Using survey data from 504 Muslims, we show that both procedural justice and police effectiveness are positively associated with Muslims’ trust in the police and negatively associated with contempt across both policing contexts. We also find that contempt is negatively associated with trust in both policing contexts. Finally, our findings show that contempt partially mediates the relationship between procedural justice and trust and between police effectiveness and trust. These findings reinforce the need for police to draw on procedurally fair and effective service delivery when engaging with minority groups who report strained relations with police. Our findings also highlight the need for scholars to closely consider distinct emotions as an important theoretical mechanism linking discriminatory police treatment with public (dis)trust of police.

Automatic cinematography for body movement involved virtual communication

AbstractThe emergence of novel AI technologies and increasingly portable wearable devices have introduced a wider range of more liberated avenues for communication and interaction between human and virtual environments. In this context, the expression of distinct emotions and movements by users may convey a variety of meanings. Consequently, an emerging challenge is how to automatically enhance the visual representation of such interactions. Here, a novel Generative Adversarial Network (GAN) based model, AACOGAN, is introduced to tackle this challenge effectively. AACOGAN model establishes a relationship between player interactions, object locations, and camera movements, subsequently generating camera shots that augment player immersion. Experimental results demonstrate that AACOGAN enhances the correlation between player interactions and camera trajectories by an average of 73%, and improves multi‐focus scene quality up to 32.9%. Consequently, AACOGAN is established as an efficient and economical solution for generating camera shots appropriate for a wide range of interactive motions. Exemplary video footage can be found at https://youtu.be/Syrwbnpzgx8.