Emotions Sadness Research Articles

허수경 시의 색채 이미지 연구

Black and white in Heo Soo-kyeong's poetry reveal unique color symbolism in a way of repetition and reversion. This aspect deepens and expands the emotion and the subject consciousness, which forms her own distinct poetic aesthetics. Furthermore, the black and white color images in her poems display an Assemblage effect, forming mixed and entangled aspects in one work. This is a new possibility created by a combination of disparate elements, a further level of thinking from the conventional wisdom that the two colors indicate an opposing single network of meanings in different works.
 In the case where the two color images are mixed and entangled in a single work, in Heo Soo-kyeong's poem, there appears two aspects of confrontation and coessentiality in the meaning of the subject consciousness. First of all, the two colors appear as opposing color images, which assumes the symbolism that schematizes the power structure of the strong and the weak. However, the meaning of these two colors can be interchangeable, not being fixed as an opposite between good and evil or strong and weak and, thereby presenting the poet's flexible way of thinking.
 On the other hand, in the case where the two color reveal coessential color images, they assume two aspects, negative coessentiality and positive coessentiality. In other words, both black and white reveal negative coessentiality by maximizing the emotion of sadness and depression, or positive coessentiality by aspiring after a world of purity and peace. This pattern deviates from the conventional wisdom that regards white and black as opposite colors, which is a point that reveals Heo Soo-kyeong's distinct sense of color.
 In this light, the color images of black and white forms deeper and richer semantic network when they appear to be mixed and entangled in one work than when used solely. This is the result of the poet's expanded creative imagination for the symbolism of color images, and it can be interpreted as the expression of multidimensional and fluid color aesthetics and securing new possibilities for color symbolism through the interaction of black and white.

Emotion Recognition from Manipuri Language Using MFCC and Convolution Neural Network.

Abstract: Emotion recognition is a significant part in designing a better Human- Computer Interaction (HCI) System. Increasing applications of Emotion recognition from speech has been found in Education, gaming, medicine and automobiles industries. And after the Covid-19 pandemic identification of human emotions and action upon is the need of the hour. In this paper we try to identify four emotions by training a CNN model using Manipuri speech data set. We test our model using real time speech signal. Our model identifies sad emotion accurately but fails to identify angry emotion.

Do emotions evoked by music modulate visuospatial working memory capacity? A physiological study

Previous studies have shown that emotions evoked through music can have transient effects on cognitive performance. Considering the importance of working memory (WM) in the processing of new information, in this study, we investigated the impact of positive and negative emotions evoked through music on visuospatial WM performance using a within-subjects design. Moreover, we concomitantly recorded the participants’ physiological responses during listening to musical stimuli. Seventy-eight participants were allocated to counterbalanced positive, negative, and neutral emotional inductions through music (EIM) followed by an adaptive visuospatial WM task. Results revealed that participants’ visuospatial WM performance was increased after positive EIM compared with negative and neutral EIMs transiently. We also observed increased skin conductance levels during positive EIM compared with baseline and a lower heart rate throughout positive EIM than the other conditions. Overall, these findings suggest that music evoking positive emotions can boost visuospatial WM performance. This is the first study to explore cognitive performance after EIM and physiological responses to musical stimuli simultaneously, which may have important practical implications since we engage in cognitively demanding activities after listening to music that could evoke happy or sad emotions.

Making bodies and worlds

Inspired by the intense affective experience of watching the film 120 BPM (Beats Per Minute/Battements par minute) (Robin Campillo, 2017) about the HIV/AIDS activist group ACT UP Paris, I turn to affect theory to better understand how the film did what it did, tracing the circulating emotions of joy, anger, love, sadness, frustration, and exhilaration among the protagonists on screen, between the screen and the audience, and among the audience. I argue that the film’s affective economy has a religious quality in the way it creates bodies, worlds, and communities, and more specifically, that these religious sensibilities resonate with Christian affectivities in all their complexity. Thus, my argument about the religious affects of 120 BPM also contributes to the reflection on the affective dimensions of Christianity, and religion more broadly, and on the religious dimensions of affective-embodied aesthetic experiences.

Formation of Stereotypes of Human Behaviour under the Influence of Childhood Fears

The relevance of the study is due to the presence of a relationship that occurs between the emotional and intellectual development of the individual. Negative changes in the process of emotional development of a person lead to the fact that they are unable to use other structures, in particular, intelligence, and therefore develop harmoniously. As a basic emotion, fear is an integral part of the emotional field, as is the emotion of joy, sadness, and anger. Fear performs a protective function, but it also destroys the individual if it is irrational. The emotion of fear, arising in childhood, can be fixed and transformed into more acute manifestations, that is, into phobic disorders. Personality is formed under the influence of society, so a very important role in its development is played by models of upbringing in the family and those attitudes and prescriptions that a person learns from childhood. In adulthood, these patterns are manifested in the form of stereotypical behaviour, which substantially affects a person’s quality of life. Therefore, it is important to clarify how childhood fears can affect the individual in adulthood. The purpose of this study is to establish a link between childhood fears and stereotypical behaviour. A number of methods and techniques were used to achieve this goal, including theoretical (analysis, synthesis, generalisation) and practical (D. McLain’s general tolerance for uncertainty scale; tolerance-intolerance of ambiguity questionnaire by T.V. Kornilova; questionnaire of childhood fears). The results of the study show that in the case when a person cannot safely live through fears as a child, they have an impact on the psychological well-being of the individual, which encourages the formation of psychological problems. It is proved that as a result of gender stereotypes in education, male and female fears formed in childhood differ markedly. Therewith, it is noted that certain stereotypes have a positive meaning in a person’s life if they do not suppress their psychological development. The research materials will be useful for practical psychologists in the education and social fields, and teachers

Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset.

Emotion identification is an essential task for human-computer interaction systems. Electroencephalogram (EEG) signals have been widely used in emotion recognition. So far, there have been several EEG-based emotion recognition datasets that the researchers have used to validate their developed models. Hence, we have used a new ICBrainDB EEG dataset to classify angry, neutral, happy, and sad emotions in this work. Signal processing-based wavelet transform (WT), tunable Q-factor wavelet transform (TQWT), and image processing-based histogram of oriented gradients (HOG), local binary pattern (LBP), and convolutional neural network (CNN) features have been used extracted from the EEG signals. The WT is used to extract the rhythms from each channel of the EEG signal. The instantaneous frequency and spectral entropy are computed from each EEG rhythm signal. The average, and standard deviation of instantaneous frequency, and spectral entropy of each rhythm of the signal are the final feature vectors. The spectral entropy in each channel of the EEG signal after performing the TQWT is used to create the feature vectors in the second signal side method. Each EEG channel is transformed into time-frequency plots using the synchrosqueezed wavelet transform. Then, the feature vectors are constructed individually using windowed HOG and LBP features. Also, each channel of the EEG data is fed to a pretrained CNN to extract the features. In the feature selection process, the ReliefF feature selector is employed. Various feature classification algorithms namely, k-nearest neighbor (KNN), support vector machines, and neural networks are used for the automated classification of angry, neutral, happy, and sad emotions. Our developed model obtained an average accuracy of 90.7% using HOG features and a KNN classifier with a tenfold cross-validation strategy.

Exploring public opinion about telehealth during COVID-19 by social media analytics

While COVID-19 catalyzed the acceptance and use of telehealth, our understanding of how it is perceived by multi-stakeholders such as patients, clinicians, and health authorities is limited. Drawing on social media analytics, this research examines social media discourses and users’ opinions about telehealth during the COVID-19 pandemic. It applies natural language processing and deep learning to explore word of mouth on telehealth with a contextualized focus on the COVID-19 pandemic. We conducted topic modeling, sentiment analysis, and emotion analysis (fearful, happy, sad, surprised, and angry emotions). The topic modeling analysis led to the identification of 18 topics, representing 6 themes of digital health service delivery, pandemic response, communication and promotion, government action, health service domains (e.g. mental health, cancer, aged care), as well as pharma and drug. The sentiment analysis revealed that while most opinions expressed in tweets were positive, the public expressed mostly negative opinions about certain aspects of COVID-19 such as lockdowns and cyberattacks. Emotion analysis of tweets showed a dominant pattern of fearful and sad emotions in particular topics. The results of this study that inductively emerged from our social media analysis can aid public health authorities and health professionals to address the concerns of telehealth users and improve their experiences.

Hampered gamma oscillations induced by sad emotion underlying suicide attempt in major depressive disorder.

Major depressive disorder (MDD) is associated with high suicidality, especially for those with suicide attempt (SA). Although impaired oscillatory activity has been previously reported in patients with SA, little is known about precise temporal-spatial variability of its neural dynamics. To solve this, the current study probed the spectral power and network interactions underlying SA in MDD. The present study recruited 104 subjects including 56 subjects with MDD (30 with SA and 26 without SA) and 48 healthy controls, who performed sad expressions recognition task during magnetoencephalography (MEG) recording. By investigating source-reconstructed MEG-data, brain states representing different task stages were estimated from a Hidden Markov model. Spectrum power and network connectivity were compared via Gaussian Mixture Models, and fractional occupancy (FO) of states were compared via an independent F-test. Brain states were corresponding to various frequencies (theta/beta/low gamma/ high gamma). In low gamma band (35-45 Hz), the early visual state exhibited increased activation and hyper inter-network connectivity between visual regions and the limbic system, while the middle fronto-parietal state exhibited attenuated activation and decreased intra-network connectivity within fronto-parietal regions in SA group. Crucially, FO values of these two states were significantly correlated with the suicide risks. Suicide behavior of patients with MDD was significantly associated with aberrant oscillations in low gamma band. Elevated oscillations in occipital cortices and attenuated oscillations in fronto-parietal cortices were significantly associated with SA. Manifesting sadness indulging and reckless decision-making, the hampered temporal characteristics could help explain the neural-electric basis of SA.

Emotional perceptions of people exposed to green and blue spaces in forest parks of cities at rapid urbanization regions of East China

Urbanization modifies the landscape with green and blue spaces (GBS), which further leads to a functional change along an urban-rural gradient. Emotional improvement is a critical service of GBS, which may be perceived and exposed through facial expressions by visitors. How people react, however, may vary at different locations of a city at varied phases of urbanization. In this study, happy and sad emotions were rated as scores from 7965 Sina-Weibo users who visited 77 GBS across 49 cities of East China in 2020. GBS were located in different regions of a city, either near downtown or in more rural-like regions. Compared to cities near the Hu Huanyong line, those along the eastern coast were built with parks that had smaller green spaces at lower elevations in locations near downtown. They also had larger blue spaces in parks at suburban areas of the same cities. People expressed more happiness in GBS in regions closer to remote rural regions, or in cities further from the eastern coast. Larger green spaces were associated with by the presentation more smiles in parks near downtown, while experiences in large blue spaces evoked positive emotions at suburban areas. Overall, GBS in population-dense regions of more developed cities can be perceived as an activation of exposing higher depression by visitors. More smiles can be exposed in GBS with a large green space near downtown, or with a large blue space at suburban regions of a city in East China.

A non-invasive machine learning mechanism for early disease recognition on Twitter: The case of anemia

Social media sites, such as Twitter, provide the means for users to share their stories, feelings, and health conditions during the disease course. Anemia, the most common type of blood disorder, is recognized as a major public health problem all over the world. Yet very few studies have explored the potential of recognizing anemia from online posts. This study proposed a novel mechanism for recognizing anemia based on the associations between disease symptoms and patients' emotions posted on the Twitter platform. We used k-means and Latent Dirichlet Allocation (LDA) algorithms to group similar tweets and to identify hidden disease topics. Both disease emotions and symptoms were mapped using the Apriori algorithm. The proposed approach was evaluated using a number of classifiers. A higher prediction accuracy of 98.96 % was achieved using Sequential Minimal Optimization (SMO). The results revealed that fear and sadness emotions are dominant among anemic patients. The proposed mechanism is the first of its kind to diagnose anemia using textual information posted on social media sites. It can advance the development of intelligent health monitoring systems and clinical decision-support systems.

Emotional characteristic analysis of human gait while real-time movie viewing.

Emotion recognition is useful in many applications such as preventing crime or improving customer satisfaction. Most of current methods are performed using facial features, which require close-up face information. Such information is difficult to capture with normal security cameras. The advantage of using gait and posture over conventional biometrics such as facial features is that gaits and postures can be obtained unobtrusively from faraway, even in a noisy environment. This study aims to investigate and analyze the relationship between human emotions and their gaits or postures. We collected a dataset made from the input of 49 participants for our experiments. Subjects were instructed to walk naturally in a circular walking path, while watching emotion-inducing videos on Microsoft HoloLens 2 smart glasses. An OptiTrack motion-capturing system was used for recording the gaits and postures of participants. The angles between body parts and walking straightness were calculated as features for comparison of body-part movements while walking under different emotions. Results of statistical analyses show that the subjects' arm swings are significantly different among emotions. And the arm swings on one side of the body could reveal subjects' emotions more obviously than those on the other side. Our results suggest that the arm movements together with information of arm side and walking straightness can reveal the subjects' current emotions while walking. That is, emotions of humans are unconsciously expressed by their arm swings, especially by the left arm, when they are walking in a non-straight walking path. We found that arm swings in happy emotion are larger than arm swings in sad emotion. To the best of our knowledge, this study is the first to perform emotion induction by showing emotion-inducing videos to the participants using smart glasses during walking instead of showing videos before walking. This induction method is expected to be more consistent and more realistic than conventional methods. Our study will be useful for implementation of emotion recognition applications in real-world scenarios, since our emotion induction method and the walking direction we used are designed to mimic the real-time emotions of humans as they walk in a non-straight walking direction.

Mapping seasonal sentiments of people visiting blue spaces in urban wetlands: A pilot study on inland cities of China

To evoke positive human emotions is a critical goal of blue spaces in urban wetland parks. However, information is still scarce on how people self-express across the spatiotemporal spectrum when they come across wetlands which include varying levels of elevation in a single landscape and microclimate. In this study, 30 urban wetland parks were selected from 17 cities in Central China, where a total of 1,184 portrait photos of visitors were obtained from a social media platform (Sina Weibo) to analyze their expressed sentiments by rating facial expression scores of happy and sad emotions and net positive emotion index (NPE; happy-score minus sad-score) in 2020. Landscape metrics were remotely evaluated for every wetland park, and microclimatic factors were obtained for the days when the photos were taken. Based on regressions of park-level data, blue-space areas could be perceived as a positive driver to trigger happiness in spring (regression coefficient [RC] of 0.20), but it triggered negative emotions in autumn (RC of −2.98). The higher elevation areas triggered positive emotions in summer and autumn (RC of 1.35 × 10−3), but extreme daily temperature, air humidity, and wind velocity together triggered sadness (RC of 0.11, 0.03, and 0.51, respectively). Mapped distribution of the area and corresponding emotions showed that visiting blue space evoked more smiles in wetland parks of northern Hunan, southern Hubei, and eastern Anhui in spring. Blue spaces in Shanxi and northwestern Hebei evoked better moods in autumn. Smaller blue spaces in wetlands located at higher elevations were recommended for nature enthusiasts in warm seasons to overcome the prevalent sadness characteristic of that time of the year and location.

Expecting in Unexpected Times

This article explores the complexities and contingencies of pregnancy during the COVID-19 pandemic. The author uses autoethnography to describe the lived experience of navigating the conflicting feelings and nuances of daily life as an expectant person during a global health crisis. In particular, the author is attuned to how one must grapple with both the positive feelings of joy and excitement that accompany many pregnancies, with the more somber emotions of guilt and sadness from losing out on many of the anticipated normalcies of the pregnancy journey. This narrative highlights emergent themes that are likely relatable to many pregnant people at this particular moment in time due to framing experiences within a larger societal context and through a scholarly, sociological lens.

Pathos in the Poetry of COVID-19 Pandemic

Poetry written during global pandemic of COVID-19 display pathos. The present article explores the use of pathos in four of the pandemic time poems. The study aims to unveil the predicament that the earth and humans endured during that time. For the analysis, theory of pathos has been taken as a tool. Pathos is one of the persuasive techniques that evokes the emotion of pity and sympathy to the audience or the readership on the suffering and sadness of the characters and the speakers of the rhetoric. This article also discusses vivid pictures of the pathetic state of the characters in the poems that is similar to the state of the entire humans who underwent it during pandemic. The present inquiry concludes that the poetry written during pandemic evokes the strong positive emotion of joy, excitement and hope, and the negative emotion of fear, sadness, agony and sympathy as well in the mind of the readers. Doing so, it establishes the close connection between the readership and the poets who had the same emotions and feelings of hope and excitement, and pain and sufferings during the composition of the poems.

Acts of Official Grief on the Berlin Museum “Trauerrelief” (AMP 12411)

Grief is a human feeling that accompanies sad events or the loss of dear ones. Acts of grief were represented in ancient Egyptian funerals for both males and females. Attendants of a funeral vary between family members, friends, professional mourners, official representatives - in case of high officials- and priests who were involved either in mummification or recitation of prayers. The current study is analyzing a scene of the funeral of the High Priest of Ptah, Ptahemhat-Ty. The relief came from Saqqara and is now a famous piece in Berlin Museum (AMP 12411). Grief on this relief came in two levels; the family level and the official level. The acts performed in the funeral vary between emotional acts and conservative acts. The sons of the deceased acted in distress and deep sadness while officials were sad in discreet and demure way. Both acts were expressed in gestures and facial features. Employing a descriptive analytical methodology it is deduced that official grief is conventional and conservative probably to maintain the image of the state with the exception of one or two officials turning the other way to hide their emotions of sadness from the public. Similar scenes of funerals of high officials show in most cases the same gestures represented on the Berlin Trauerrelief. Keywords: mourners, funeral, official grief

The effect of cognitive reappraisal and expression suppression on sadness and the recognition of sad scenes: An event-related potential study.

Previous studies have found differences in the cognitive and neural mechanisms between cognitive reappraisal and expression suppression in the regulation of various negative emotions and the recognition of regulated stimuli. However, whether these differences are valid for sadness remains unclear. As such, we investigated the effect of cognitive reappraisal and expression suppression on sadness regulation and the recognition of sad scenes adopting event-related potentials (ERPs). Twenty-eight healthy undergraduate and graduate students took part in this study. In the regulation phase, the participants were asked to down-regulation, expressive suppression, or maintain their sad emotion evoked by the sad images, and then to perform an immediately unexpected recognition task involving the regulated images. The behavioral results show that down-regulation reappraisal significantly diminished subjective feelings of sadness, but expressive suppression did not; both strategies impaired the participants’ recognition of sad images, and expressive suppression had a greater damaging effect on the recognition of sad images than down-regulation reappraisal. The ERP results indicate that reappraisal (from 300 ms to 1,500 ms after image onset) and expressive suppression (during 300–600 ms) significantly reduced the late positive potential (LPP) induced by sadness. These findings suggest that down-regulation reappraisal and expression suppression can effectively decrease sadness, and that down-regulation reappraisal (relative to expression suppression) is a more effective regulation strategy for sadness. Both strategies impair the recognition of sad scenes, and expression suppression (compared to down-regulation reappraisal) leads to relatively greater impairment in the recognition of sad scenes.

Awareness of ocean literacy principles and ocean conservation engagement among American adults

Ocean literacy influences ocean conservation and might have an impact on sustainable development practices. We investigated the knowledge American adults possess concerning ocean literacy principles and ocean conservation behaviors by asking participants (N=1226) to complete an online survey on ocean literacy principles, health and well-being benefits of time spent in or by the ocean, emotions that result in behavioral change for conservation, and conservation actions. We used a representative sample of the US population by age, gender, and location. The majority of participants were aware of the ocean literacy principle, that the ocean supports a great diversity of life and ecosystems (82%). Calmness, stress reduction, reduced depression, awe, connection with nature, greater life satisfaction, improved happiness, and a feeling of a spiritual connection or refuge were all associated with the ocean environment’s emotional health benefits. Monthly beach trips were associated with the physical health benefits of spending time near the ocean. Engagement in ocean conservation action was associated with multiple factors, including awareness of ocean literacy principles (the ocean is largely unexplored, ocean health and human health are connected, and the ocean regulates our climate and weather), monthly visits to the ocean, emotions of sadness and awe, feeling a connection to nature, living 10 miles or less to the ocean, and viewing nature documentaries and Instagram. Findings from this study may provide ideas how to engage the American public in understanding, valuing, and caring for our shared life sustaining ocean.

Effects of immersion and navigation agency in virtual environments on emotions and behavioral intentions

We present a study investigating the question whether and how people’s intention to change their environmental behavior depends on the degrees of immersion and freedom of navigation when they experience a deteriorating virtual coral reef. We built the virtual reef on top of a biologically sound model of the ecology of coral reefs, which allowed us to simulate the realistic decay of reefs under adverse environmental factors. During their experience, participants witnessed those changes while they also explored the virtual environment. In a two-factorial experiment (N = 224), we investigated the effects of different degrees of immersion and different levels of navigation freedom on emotions, the feeling of presence, and participants’ intention to change their environmental behavior. The results of our analyses show that immersion and navigation have a significant effect on the participants’ emotions of sadness and the feeling of helplessness. In addition, we found a significant effect, mediated by the participants’ emotions, on the intention to change their behavior. The most striking result is, perhaps, that the highest level of immersion combined with the highest level of navigation did not lead to the highest intentions to change behavior. Overall, our results show that it is possible to raise awareness of environmental threats using virtual reality; it also seems possible to change people’s behavior regarding these threats. However, it seems that the VR experience must be carefully designed to achieve these effects: a simple combination of all affordances offered by VR technology might potentially decrease the desired effects.

The effects of mixed emotional appeals in leveraging paradox brands

This research examines the concept of paradox brands – that is, brands with contradictory brand personality associations (e.g., rugged yet sophisticated) – and explores how to increase favorable evaluations of such brands using specific emotional appeals. In this regard, important life events such as graduation or memories of a grandmother can elicit the mixed emotions of happiness and sadness at the same time. This research reports three experimental studies conducted in different markets (the U.S. and the UK) that demonstrate mixed emotional appeals (sadness and happiness) are more persuasive than positive emotional appeals (happiness) for enhancing favorable consumer evaluations of paradox brands. This is because experiencing mixed emotions increases cognitive flexibility. Further, the effect of mixed emotional appeals is attenuated at high construal levels. This research offers meaningful theoretical and practical implications to the literatures on brand personality and mixed emotions by highlighting the potential of mixed emotions for promoting paradox brands.

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network.

This work intends to classify and integrate music genres and emotions to improve the quality of music education. This work proposes a web image education resource retrieval method based on semantic network and interactive image filtering for a music education environment. It makes a judgment on these music source data and then uses these extracted feature sequences as the emotions expressed in the model of the combination of Long Short-Term Memory (LSTM) and Attention Mechanism (AM), thus judging the emotion category of music. The emotion recognition accuracy has increased after improving LSTM-AM into the BiGR-AM model. The greater the difference between emotion genres is, the easier it is to analyze the feature sequence containing emotion features, and the higher the recognition accuracy is. The classification accuracy of the excited, relieved, relaxed, and sad emotions can reach 76.5%, 71.3%, 80.8%, and 73.4%, respectively. The proposed interactive filtering method based on a Convolutional Recurrent Neural Network can effectively classify and integrate music resources to improve the quality of music education.