Multiple Emotions Research Articles

Frequency-dependent genetic modulation of neuronal oscillations: a combined transcriptome and resting-state functional MRI study.

Neuronal oscillations within certain frequency bands are assumed to associate with specific neural processes and cognitive functions. To examine this hypothesis, transcriptome-neuroimaging spatial correlation analysis was applied to resting-state functional magnetic resonance imaging data from 793 healthy individuals and gene expression data from the Allen Human Brain Atlas. We found that expression measures of 336 genes were correlated with fractional amplitude of low-frequency fluctuations (fALFF) in the slow-4 band (0.027-0.073Hz), whereas there were no expression-fALFF correlations for the other frequency bands. Furthermore, functional enrichment analyses showed that these slow-4 fALFF-related genes were mainly enriched for ion channel, synaptic function, and neuronal system as well as many neuropsychiatric disorders. Specific expression analyses demonstrated that these genes were specifically expressed in brain tissue, in neurons, and during the late stage of cortical development. Concurrently, the fALFF-related genes were linked to multiple behavioral domains, including dementia, attention, and emotion. In addition, these genes could construct a protein-protein interaction network supported by 30 hub genes. Our findings of a frequency-dependent genetic modulation of spontaneous neuronal activity may support the concept that neuronal oscillations within different frequency bands capture distinct neurobiological processes from the perspective of underlying molecular mechanisms.

From Intricacy to Conciseness: A Progressive Transfer Strategy for EEG-Based Cross-Subject Emotion Recognition.

Emotion plays a significant role in human daily activities, and it can be effectively recognized from EEG signals. However, individual variability limits the generalization of emotion classifiers across subjects. Domain adaptation (DA) is a reliable method to solve the issue. Due to the nonstationarity of EEG, the inferior-quality source domain data bring negative transfer in DA procedures. To solve this problem, an auto-augmentation joint distribution adaptation (AA-JDA) method and a burden-lightened and source-preferred JDA (BLSP-JDA) approach are proposed in this paper. The methods are based on a novel transfer idea, learning the specific knowledge of the target domain from the samples that are appropriate for transfer, which reduces the difficulty of transfer between two domains. On multiple emotion databases, our model shows state-of-the-art performance.

A Review of Foreign Language Learners' Emotions.

In the past half century, the research on emotions of foreign language learners (FLL) has shifted the focus from an exclusive analysis of negative emotions to a more holistic study of both negative emotions and positive emotions, and currently to mediators of multiple emotions. Of the FLL’s negative emotions, foreign language anxiety (FLA) attracts the most attention. Researchers have widely discussed the relationship between FLA and foreign language achievement, the influencing factors of FLA, the dynamicity of FLA as well as regulation and intervention strategies of FLA. Foreign language enjoyment (FLE) is the most heavily studied research subject on FLL’s positive emotions. Researchers generally focus on the influencing factors of FLE, the dynamicity of FLE and the relationship between FLE and FLA. With the implementation of holistic education, emotional intelligence (EI), a mediator of multiple emotions of FLL, has been becoming a newly hot topic in the field of FLL’s emotions. By reviewing the previous studies, this paper proposes that the future research on FLL emotion needs to expand the research perspectives, enrich the research topics, and innovate the research methods.

Neuromarketing and Global Branding Reaction Analysis Based on Real-Time Monitoring of Multiple Consumer's Biosignals and Emotions

Neuromarketing and Global Branding Reaction Analysis Based on Real-Time Monitoring of Multiple Consumer's Biosignals and Emotions

Cross-Event User Reaction Prediction on a Social Network Platform

Social network surges with multiple tweets with mixture of multiple emotions by many users when events like rape, robbery, war and murder, we use this user data to analyze user emotions between cross-events and try to predict user reactions for the next possible such event. Cross-events are a series of events that belong under the same umbrella of topics and are related to the events occurring prior to it. The proposed system solve this problem using collaborative filtering using Topical and Social context. The Text Rank Algorithm is an unsupervised algorithm used for keyword extraction. Count Vectorizer is used on preprocessed text to get the frequency of words throughout the text which is used as training data to get a probability of emotion using a logistic regression model. We incorporated social context along with topical context to account for homophily and used the Low-rank matrix factorization method for user-topic prediction. The model as an output gives a total of 8 emotions which include Shame, Disgust, Anger, Fear, Sadness, Neutral, Surprise and Joy. Finally, the model is able to predict emotions with an accuracy of 95% considering cross events.

Discrete Emotion Profiles in Old Age: Stability and Change for Better or Worse

Although discrete emotions can change in salience across adulthood, little is known about developmental shifts in the co-occurrence of multiple discrete emotions. The present study (n=389, Mage=73) adopted a person-centered approach to identify stability and change in commonly-occurring profiles of calmness, excitement, sadness, and anger. Daily emotions were assessed over 1-week periods at baseline and two years later. Latent class analyses yielded consistent 3-profile solutions at both waves: a positive emotion (high calmness-moderate excitement-low sadness and anger), a mixed emotion (moderate/high calmness-moderate excitement, sadness, and anger), and an apathetic emotion profile (low calmness, excitement, sadness, and anger). Latent transition analyses revealed both stability (82% remained in the same profile) and change (18% changed profiles) in profile membership. Higher baseline optimism and fewer chronic conditions were associated with adaptive changes in profile membership. Findings point to the importance of considering the co-occurrence of distinct emotions in studying emotional aging.

Image emotion distribution learning based on enhanced fuzzy KNN algorithm with sparse learning

With the trend of people expressing opinions and emotions via images online, increasing attention has been paid to affective analysis of visual content. Traditional image affective analysis mainly focuses on single-label classification, but an image usually evokes multiple emotions. To this end, emotion distribution learning is proposed to describe emotions more explicitly. However, most current studies ignore the ambiguity included in emotions and the elusive correlations with complex visual features. Considering that emotions evoked by images are delivered through various visual features, and each feature in the image may have multiple emotion attributes, this paper develops a novel model that extracts multiple features and proposes an enhanced fuzzy k-nearest neighbor (EFKNN) to calculate the fuzzy emotional memberships. Specifically, the multiple visual features are converted into fuzzy emotional memberships of each feature belonging to emotion classes, which can be regarded as an intermediate representation to bridge the affective gap. Then, the fuzzy emotional memberships are fed into a fully connected neural network to learn the relationships between the fuzzy memberships and image emotion distributions. To obtain the fuzzy memberships of test images, a novel sparse learning method is introduced by learning the combination coefficients of test images and training images. Extensive experimental results on several datasets verify the superiority of our proposed approach for emotion distribution learning of images.

Similar brains blend emotion in similar ways: Neural representations of individual difference in emotion profiles

Our daily emotional experience is a complex construct that usually involves multiple emotions blended in a context-dependent manner. However, the co-occurring and context-dependent nature of human emotions was understated in previous studies when addressing the individual difference in emotional experiences. The present study proposed a situated and blended ‘profile’ perspective to characterize individualized emotional experiences. Eighty participants watched a series of emotional videos with their EEG recorded, and the individual differences in their emotion profiles were measured as the vector distances between their multidimensional emotion ratings for these video stimuli. This measure was found to be a reliable descriptor of individualized emotional experiences and could efficiently predict classical emotional complexity indices. More importantly, inter-subject representational analyses revealed that similar emotion profiles were associated with similar delta-band activities over the prefrontal and temporo-parietal regions and similar theta-band activities over the frontal regions. Furthermore, left- and right-lateralized temporo-parietal representations were observed for positive and negative emotion profiles, respectively. Our findings demonstrate the potential of taking a ‘profile’ perspective for understanding individual differences in human emotions.

Dynamic Functional Network Connectivity Associated with Musical Emotions Evoked by Different Tempi.

Introduction: Music tempo has strong clinical maneuverability and positive emotional effect in music therapy, which can directly evoke multiple emotions and dynamic neural changes in the whole brain. However, the precise relationship between music tempo and its emotional effects remains unclear. The present study aimed to investigate the dynamic network connectivity (dFNC) associated with emotions elicited by music at different tempi. Materials and Methods: We obtained emotion ratings of fast-tempo (155-170 beats per minute [bpm]), middle-tempo (90 bpm), and slow-tempo (50-60 bpm) piano music from 40 participants both during and after functional magnetic resonance imaging (fMRI). Group independent component analysis (ICA), sliding time window correlations, and k-means clustering were used to assess the dFNC of fMRI data. Paired t-tests were conducted to compare the difference of neural networks. Results: (1) Fast music was associated with higher ratings of emotional valence and arousal, which were accompanied with increasing dFNC between somatomotor (SM) and cingulo-opercular (CO) networks and decreasing dFNC between frontoparietal and SM networks. (2) Even with stronger activation in auditory (AUD) networks, slow music was associated with weaker emotion than fast music, with decreasing functional network connectivity across the brain and the participation of default mode (DM). (3) Middle-tempo music elicited moderate emotional activation with the most stable dFNC in the whole brain. Conclusion: Faster music increases neural activity in the SM and CO regions, increasing the intensity of the emotional experience. In contrast, slower music was associated with decreasing engagement of AUD and stable engagement of DM, resulting in a weak emotional experience. These findings suggested that the time-varying aspects of functional connectivity can help to uncover the dynamic neural substrates of tempo-evoked emotion while listening to music.

Capturing Emotion Distribution for Multimedia Emotion Tagging

Multimedia collections usually induce multiple emotions in audiences. The data distribution of multiple emotions can be leveraged to facilitate the learning process of emotion tagging, yet has not been thoroughly explored. To address this, we propose adversarial learning to fully capture emotion distributions for emotion tagging of multimedia data. The proposed multimedia emotion tagging approach includes an emotion classifier and a discriminator. The emotion classifier predicts emotion labels of multimedia data from their content. The discriminator distinguishes the predicted emotion labels from the ground truth labels. The emotion classifier and the discriminator are trained simultaneously in competition with each other. By jointly minimizing the traditional supervised loss and maximizing the distribution similarity between the predicted emotion labels and the ground truth emotion labels, the proposed multimedia emotion tagging approach successfully captures both the mapping function between multimedia content and emotion labels as well as prior distribution in emotion labels, and thus achieves state-of-the-art performance for multiple emotion tagging, as demonstrated by the experimental results on four benchmark databases.

A 10.13µJ/Classification 2-Channel Deep Neural Network Based SoC for Negative Emotion Outburst Detection of Autistic Children.

An electroencephalogram (EEG)-based non-invasive 2-channel neuro-feedback SoC is presented to predict and report negative emotion outbursts (NEOB) of Autistic patients. The SoC incorporates area-and-power efficient dual-channel Analog Front-End (AFE), and a deep neural network (DNN) emotion classification processor. The classification processor utilizes only the two-feature vector per channel to minimize the area and overfitting problems. The 4-layers customized DNN classification processor is integrated on-sensor to predict the NEOB. The AFE comprises two entirely shared EEG channels using sampling capacitors to reduce the area by 30%. Moreover, it achieves an overall integrated input-referred noise, NEF, and crosstalk of 0.55 µVRMS, 2.71, and -79 dB, respectively. The 16 mm2 SoC is implemented in 0.18 um 1P6M, CMOS process and consumes 10.13 μJ/classification for 2 channel operation while achieving an average accuracy of >85% on multiple emotion databases and real-time testing.

Sign language teaching technological advances and differences in international contexts

Sign language teaching technological advances and differences in international contexts

Detecting Multi-label emotions from code-mixed Facebook Status Updates

Objectives: With the growth of Social Media and the increasing use of English-Hindi (Hinglish) in linguistically diverse countries such as India, it is becoming increasingly important to analyse Hinglish-language content on Social Media platforms like Facebook. Prior sentiment and emotion analyses have only focused on single-label classification, ignoring the possibility of coexisting emotions within one instance. By analysing code-mixed Facebook status updates, the study aims to investigate multiple emotions. Method: 15,995 English-Hindi mixed Facebook status updates are annotated with emotions like joy, sadness, anger, fear, trust, disgust, surprise, anticipation, and love. Different pre-processing techniques are used to normalize the noisy data to produce more accurate results. We apply five different multi-level classification algorithms with word-level and character n-gram approaches to test the best classification results. Findings: The results of the experiment indicate that a status update can evoke multiple emotions rather than just one. Precision, recall, F1 score, and accuracy using both Micro and Macro averaging are used to evaluate the performance of different classifiers. As compared to other classification algorithms, the Classifier Chains algorithm with its 2-6-gram approach has the highest accuracy of 86% with a precision of 0.98. As compared to other classifiers, the Classifier Chains algorithm offered better results due to its ability to consider the correlations between class labels. Applications: The article focuses on the multi-label emotion classification task, which examines whether a Facebook status update shows none, one, or more of the nine emotions as outlined by Plutchik’s wheel of emotions. Considering the emotion of a text can support decision-making processes in various ways. Keywords: Social Media; Emotion Analysis; Code-Mixed; MultiLabel 29 Classification; Emojis; Lexicons

TGSL-Dependent Feature Selection for Boosting the Visual Sentiment Classification

The automatic recognition of the emotions in still images is inherently more challenging than other visual recognition tasks, such as scene recognition, object classification and semantic image classification, as it involves a higher level of abstraction in the human cognition perspective. Symmetry can be found in many objects in the nature and can be used for many purposes such as object detection and recognition. Furthermore, rotating and flipping of the image is employed based on symmetry for training the classifier for the most accurate classification. Hence, there is a need to handle effectively large intra-class variance, scalability and subjectivity during recognition, and it is inherently ambiguous as an image can evoke multiple emotions. To address these issues, many of the existing works focus on improving the image representations. It is motivated by the observation that both global distributions and local image regions carry massive sentiments. In this research, three different pre-trained architectural models are implemented, and the classification performance of binary sentiment classification is examined on five widely-used effective datasets. Moreover, the features from the pre-trained models are selected optimally using the proposed Teaching Gaining Sharing Learning (TGSL) algorithm, which is the major contribution of the research. Extensive experiment results on the five datasets demonstrate that the proposed Visual sentiment analysis based on the TGSL algorithm with data augmentation achieved an improved performance compared to all other conventional techniques. The proposed framework uses the pre-trained model and never utilized any hand-crafted features, boosting the mean accuracy, sensitivity, and specificity to 99.11%, 99.31%, and 99.22%, respectively, for abstract dataset.

A race against time: couples’ lived diagnostic journeys to young-onset dementia

Purpose Young onset dementias (YOD) – typically defined by symptom onset before age 65 – are frequently overlooked in medical and community settings. Persons with YOD and their spouses face logistical and emotional challenges on their journey to a diagnosis (e.g. uncertainty about symptoms, lack of medical knowledge, emotional distress). An in-depth understanding of couples’ experiences before and immediately following a YOD diagnosis is warranted to inform early psychosocial services for couples. Methods We utilized dyadic qualitative semi-structured interviews (N = 23) to better understand the nuances of couples’ pre-diagnostic and diagnostic experiences with YOD. We used a hybrid of deductive and inductive analytic strategies to identify couple-level themes. Results Fifteen couple-level themes were extracted within four domains: (1) early indicators of symptoms, (2) obtaining a diagnosis, (3) experiences with health care providers, and (4) emotional reactions to the diagnosis. Couples expressed difficulties communicating about early symptoms, receiving accurate and timely diagnostic information, and managing multiple emotions. They described the value of working together to manage care, gain information, establish positive relationships with providers, and promote adjustment. Conclusion Findings highlight the shared experiences of couples during the early stages of YOD, including the importance of strong communication with each other and providers. Early and accessible psychosocial services that help couples cope with and communicate about individual and shared stressors are warranted.

Co‐brand partnerships making space for the next black girl: Backlash in social justice branding

AbstractViral influence of consumer reviews can change the strategic intent of co‐branding relationships between minority entrepreneurial startups and established corporations. When social justice positioning is involved, the intent is often to support minority businesses. We acknowledge this strategy as social justice branding. However, such alliances can trigger vitriolic responses. This backlash is an expression of free speech, and social media provides an obscure facilitative environment. The challenges of social justice branding are complex. We examine consumer online reviews of The Honey Pot and Target Corporation partnership, to understand the implications of social justice branding in practice. Using Leximancer analysis, we find that social justice branding elicits multiple emotions that empower action. Some consumers accepted the collaboration while others resisted. Analyzing the basis of the resistance revealed some consumers expressed racist and shameful behaviors displaying disagreement. Backlash reflected willful ignorance releasing pent up prejudices and disseminating viral‐like negative information as weapons of retaliation against brands and marginalized communities. We contribute a conceptual model integrating theoretical frames of social justice, social marketing, brand activism and free speech elucidating the impact of consumer response to social justice branding. The findings and model add a lens to further explore race in the marketplace.

The Sexual OBjectification and EMotion database: A free stimulus set and norming data of sexually objectified and non-objectified female targets expressing multiple emotions

Sexual objectification – perceiving or treating a woman as a sexual object – is a widespread phenomenon. Studies on sexual objectification and its consequences have grown dramatically over the last decades covering multiple and diverse areas of research. However, research studying sexual objectification might have limited internal and external validity due to the lack of a controlled and standardized picture database. Moreover, there is a need to extend this research to other fields including the study of emotions. Therefore, in this paper we introduce the SOBEM Database, a free tool consisting of 280 high-resolution pictures depicting objectified and non-objectified female models expressing a neutral face and three different emotions (happiness, anger, and sadness) with different intensity. We report the validation of this dataset by analyzing results of 134 participants judging pictures on the six basic emotions and on a range of social judgments related to sexual objectification. Results showed how the SOBEM can constitute an appropriate instrument to study both sexual objectification per se and its relation with emotions. This database could therefore become an important instrument able to improve the experimental control in future studies on sexual objectification and to create new links with different fields of research.

Alexithymia Is Associated With Deficits in Visual Search for Emotional Faces in Clinical Depression.

Background: The concept of alexithymia is characterized by difficulties identifying and describing one's emotions. Alexithymic individuals are impaired in the recognition of others' emotional facial expressions. Alexithymia is quite common in patients suffering from major depressive disorder. The face-in-the-crowd task is a visual search paradigm that assesses processing of multiple facial emotions. In the present eye-tracking study, the relationship between alexithymia and visual processing of facial emotions was examined in clinical depression.Materials and Methods: Gaze behavior and manual response times of 20 alexithymic and 19 non-alexithymic depressed patients were compared in a face-in-the-crowd task. Alexithymia was empirically measured via the 20-item Toronto Alexithymia-Scale. Angry, happy, and neutral facial expressions of different individuals were shown as target and distractor stimuli. Our analyses of gaze behavior focused on latency to the target face, number of distractor faces fixated before fixating the target, number of target fixations, and number of distractor faces fixated after fixating the target.Results: Alexithymic patients exhibited in general slower decision latencies compared to non-alexithymic patients in the face-in-the-crowd task. Patient groups did not differ in latency to target, number of target fixations, and number of distractors fixated prior to target fixation. However, after having looked at the target, alexithymic patients fixated more distractors than non-alexithymic patients, regardless of expression condition.Discussion: According to our results, alexithymia goes along with impairments in visual processing of multiple facial emotions in clinical depression. Alexithymia appears to be associated with delayed manual reaction times and prolonged scanning after the first target fixation in depression, but it might have no impact on the early search phase. The observed deficits could indicate difficulties in target identification and/or decision-making when processing multiple emotional facial expressions. Impairments of alexithymic depressed patients in processing emotions in crowds of faces seem not limited to a specific affective valence. In group situations, alexithymic depressed patients might be slowed in processing interindividual differences in emotional expressions compared with non-alexithymic depressed patients. This could represent a disadvantage in understanding non-verbal communication in groups.

Negative emotions detection on online mental-health related patients texts using the deep learning with MHA-BCNN model

Mining the emotions in the text related to mental health-care oriented is a challenging aspect, especially dealing with a long-text sequence of data. The extraction of emotions depends upon the various psychological depression factors like negative and ambiguity. Identifying these factors is the most perplexing task for every psychiatrist to treat their patients. Our study includes the deep learning (DL) models with global vector representations (GloVe) embeddings to capture the text sequence of data. We proposed a model multi-head attention with bidirectional long short-term memory and convolutional neural network (MHA-BCNN) is a pre-eminent mechanism that outperforms better than past research works for capturing the negative text-based emotions. In this paper, by using DL extracted the various negative mental-health emotions like addiction, anxiety, depression, insomnia, stress, and obsessive cleaning disorder (OCD). By using the GloVe embeddings and handled the ambiguity factors like multiple emotion words in a certain sequence. As we proposed a vigorous appliance in our research to capture and hoard the long-term dependencies. We extracted the questions related to mental health issues were posted by the patients in an online mental healthcare-oriented platform. We efficaciously handled both negative and ambiguity factors at the document level. Our suggested exemplary MHA-BCNN surmounts various aspects from preceding research works and ensued preeminent performance. Experimental results show that our proposed framework MHA-BCNN outperformed than the erstwhile research works.

A Dataset of Multidimensional and Multilingual Social Opinions for Malta’s Annual Government Budget

This paper presents three high quality social opinion datasets in the socio-economic domain, specifically Malta's annual Government Budgets of 2018, 2019 and 2020. They contain over 6,000 online posts of user-generated content in English and/or Maltese, gathered from newswires and social networking services. These have been annotated for multiple opinion dimensions, namely subjectivity, sentiment polarity, emotion, sarcasm and irony, and in terms of negation, topic and language. These datasets are a valuable resource for developing Opinion Mining tools and Language Technologies, and can be used as a baseline for assessing the state-of-the-art and for developing new advanced analytical methods for Opinion Mining. Moreover, they can be used for policy formulation, policy-making, decision-making and decision-taking. This research can also support similar initiatives in other countries, studies in the socio-economic domain and applied in other areas, such as Politics, Finance, Marketing, Advertising, Sales and Education.