Recognition Of Negative Emotions Research Articles

Research on the Application of Speech Database based on Emotional Feature Extraction in International Chinese Education and Teaching

The advanced analysis of the relationship between acoustic and emotional characteristics of speech signals can effectively improve the interactivity and intelligence of computers. Given the current status of speech recognition and the problems encountered in international Chinese education, the study proposes to extract emotional characteristics to achieve speech construction of the database. Based on considering the emotional characteristics of speech, a hybrid algorithm based on spectral sequence context features is proposed. The DBN-BP algorithm is used to process emotional data of different dimensions, and a speech database is constructed. After testing and analyzing the algorithm model, it is found that the dynamic recognition accuracy of the DBN-BP model fused with emotional features is over 90%, and the negative emotion recognition rates in the three databases are all above 60%. At the same time, the accuracy rate of the model in the algorithm comparison experiment remains above 85%, the data information extraction is relatively complete, and the average test time of less than 1s is less than 3%. The speech database based on multi-emotional feature extraction can effectively provide a new reference for the improvement of the quality of Chinese international education and the improvement of the speech recognition system.

The cross-race effect in automatic facial expression recognition violates measurement invariance.

Emotion has been a subject undergoing intensive research in psychology and cognitive neuroscience over several decades. Recently, more and more studies of emotion have adopted automatic rather than manual methods of facial emotion recognition to analyze images or videos of human faces. Compared to manual methods, these computer-vision-based, automatic methods can help objectively and rapidly analyze a large amount of data. These automatic methods have also been validated and believed to be accurate in their judgments. However, these automatic methods often rely on statistical learning models (e.g., deep neural networks), which are intrinsically inductive and thus suffer from problems of induction. Specifically, the models that were trained primarily on Western faces may not generalize well to accurately judge Eastern faces, which can then jeopardize the measurement invariance of emotions in cross-cultural studies. To demonstrate such a possibility, the present study carries out a cross-racial validation of two popular facial emotion recognition systems-FaceReader and DeepFace-using two Western and two Eastern face datasets. Although both systems could achieve overall high accuracies in the judgments of emotion category on the Western datasets, they performed relatively poorly on the Eastern datasets, especially in recognition of negative emotions. While these results caution the use of these automatic methods of emotion recognition on non-Western faces, the results also suggest that the measurements of happiness outputted by these automatic methods are accurate and invariant across races and hence can still be utilized for cross-cultural studies of positive psychology.

Negative Emotion Recognition Algorithm of Network Catchwords Based on Language Feature Dimension

The traditional negative emotion recognition algorithm has a limited language feature dimension, which leads to the inaccuracy of negative emotion recognition. In order to improve the identification and analysis of emotion in network buzzwords, the back propagation of error (BP) and the restricted Boltzmann machine (RBM) algorithms are adopted to effectively solve the problem of insufficient data for emotion analysis in different contexts. First, a method is proposed to identify negative emotions, and a deep neural network (DNN) model is constructed. Then, experiments were carried out, which used manually labeled data sets and divided them into different emotion categories, and which compared the BP algorithm, Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) for negative emotion recognition of online buzzwords. The experimental results show that the DNN model performs well in the recognition of anger, sadness, fear and disgust, with the accuracy reaching 93.56%, 93.58%, 89.84% and 88.53% respectively, which is obviously superior to the other three methods. The designed DNN model has a potential application prospect in the negative emotion recognition of online buzzwords, which can be further popularized in the future.

A Negative Emotion Recognition System with Internet of Things-Based Multimodal Biosignal Data

Previous studies to recognize negative emotions for mental healthcare have used heavy equipment directly attaching electroencephalogram (EEG) electrodes to the head, and they have proposed binary classification methods to identify negative emotions. To tackle this problem, we propose a negative emotion recognition system to collect multimodal biosignal data such as five EEG signals from an EEG headset and heart rate, galvanic skin response, and skin temperature from a smart band for classifying multiple negative emotions. This consists of an Android Internet of Things (IoT) application, a oneM2M-compliant IoT server, and a machine learning server. The Android IoT application uploads the biosignal data to the IoT server. By using the biosignal data stored in the IoT server, the machine learning server recognizes the negative emotions of disgust, fear, and sadness using a multiclass support vector machine (SVM) model with a radial basis function kernel. The experimental results demonstrate that the multimodal biosignal data approach achieves 93% accuracy. Moreover, when considering only data from the smart band, the system achieved 98% accuracy by optimizing the hyperparameters of the multiclass SVM model. Based on these results, we plan to develop a metaverse system that detects and expresses negative emotions in real time.

The role of affective touch in modulating emotion processing among preschool children

Recognizing emotional expressions is a prerequisite for understanding others’ feelings and intentions, a key component of social interactions that develops throughout childhood. In multisensory social environments, touch may be crucial for emotion processing, linking external sensory information with internal affective states. The current study investigated whether affective touch facilitates recognition of emotional expressions throughout childhood. Preschool children (N = 121 3- to 6-year-olds) were presented with different tactile stimulations followed by an emotion-matching task. Results revealed that affective touch fosters the recognition of negative emotions and increases the speed of association of positive emotions, highlighting the centrality of tactile experiences for socioemotional understanding. The current research opens new perspectives on how to support emotional recognition with potential consequences for the development of social functioning.

Menstrual cycle changes in estradiol, stress reactivity, and emotion recognition

Previous research suggests that estradiol (E2) influences mood, but the mechanisms underlying these effects remain unclear. The current study measured affect and two proposed mechanisms – psychosocial stress reactivity and emotion recognition – in the early and late follicular menstrual phases, which differ in E2 levels. 29 reproductive-aged women completed the Positive and Negative Affect Schedule – Extended (PANAS-X), a facial emotion recognition task, and the Trier Social Stress Test (TSST) in the early (EF; low E2) and late (LF; high E2) follicular phases. Visual analogue scales measured subjective stress, insecurity, rejection, irritability, and contentment throughout the TSST. Salivary E2 was measured during each menstrual phase, and salivary cortisol was assessed at baseline and at 10 and 20 minutes after the TSST. Positive affect (p=.012) was significantly higher in the LF phase, and negative affect (p=.022) was significantly higher in the EF phase. Accuracy for recognizing anger (p=.026) was significantly higher in the EF phase but not for the other emotions assessed. Cortisol AUC with respect to increase was larger in the EF phase (p=.005). Additionally, the change in E2 from the EF to the LF phase was associated with a decline in fear recognition accuracy (r=-.41, p=.028) and a decrease in post-TSST irritability (r=-.41, p=.037). These findings suggest that menstrual cycle increases in E2 are associated with increased positive affect, decreased negative affect, more favorable cortisol responses to psychosocial stress, and decreased recognition of negative emotions.

The role of emotion-related individual differences in enjoyment and masking smile judgment

BackgroundWhile some research indicates that individuals can accurately judge smile authenticity of enjoyment and masking smile expressions, other research suggest modest judgment rates of masking smiles. The current study explored the role of emotion-related individual differences in the judgment of authenticity and recognition of negative emotions in enjoyment and masking smile expressions as a potential explanation for the differences observed.MethodsSpecifically, Experiment 1 investigated the role of emotion contagion (Doherty in J Nonverbal Behav 21:131–154, 1997), emotion intelligence (Schutte et al. in Personality Individ Differ 25:167–177, 1998), and emotion regulation (Gratz and Roemer in J Psychopathol Behav Assess 26:41–54, 2004) in smile authenticity judgment and recognition of negative emotions in masking smiles. Experiment 2 investigated the role of state and trait anxiety (Spielberger et al. in Manual for the state-trait anxiety inventory, Consulting Psychologists Press, Palo Alto, 1983) in smile authenticity judgment and recognition of negative emotions in the same masking smiles. In both experiments, repeated measures ANOVAs were conducted for judgment of authenticity, probability of producing the expected response, for the detection of another emotion, and for emotion recognition. A series of correlations were also calculated between the proportion of expected responses of smile judgement and the scores on the different subscales.ResultsResults of the smile judgment and recognition tasks were replicated in both studies, and echoed results from prior studies of masking smile judgment: participants rated enjoyment smiles as happier than the masking smiles and, of the masking smiles, participants responded “really happy” more often for the angry-eyes masking smiles and more often categorized fear masking smiles as “not really happy”.ConclusionsOverall, while the emotion-related individual differences used in our study seem to have an impact on recognition of basic emotions in the literature, our study suggest that these traits, except for emotional awareness, do not predict performances on the judgment of complex expressions such as masking smiles. These results provide further information regarding the factors that do and do not contribute to greater judgment of smile authenticity and recognition of negative emotions in masking smiles.

Cloud-Edge Collaborative Depression Detection Using Negative Emotion Recognition and Cross-Scale Facial Feature Analysis

Depression is a mental disorder that causes pain to people and society and is also the largest cause of disability in the world. Intelligent early screening of depression is of great benefit for patients to obtain better diagnoses and treatment. However, previous low-precision detection methods based on facial vision heavily rely on computing resources, which hinders the wide application of automatic depression diagnoses. Therefore, this article proposes an intelligent method for multiscene automatic depression symptom detection, which uses an efficient and convenient cloud-edge collaboration framework combined with negative emotion monitoring and cross-scale facial feature analysis. We deploy a shallow model (EdgeER) on the edge server and a deep model (C-DepressNet) on the cloud server. EdgeER is used to quickly detect negative user emotions and screen user data. C-DepressNet is used to analyze degrees of depression with high precision. The experimental results show that our cloud-edge collaboration framework has superior performance in depression detection accuracy and service response times.

Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems

Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems

Sentiment recognition model of EEG signals combined with one-dimensional convolution and BiBASRU-AT

To address the problems of low accuracy of EEG emotion sentiment and insufficient feature extraction ability of recurrent model, an EEG sentiment recognition model combining one-dimensional convolution and BiBASRU-AT is proposed. The data set is preprocessed in segments to expand the number of samples, and 62 channel local emotional features are extracted from one-dimensional convolution; The built-in self-attention simple recurrent unit is constructed to capture the multi-channel fusion features and the dependence between channels. The soft attention mechanism identifies the key features that have a great impact on the identification of emotional tendencies, and the linear layer outputs the positive, neutral and negative emotion recognition results. The experimental results on the EEG data set(SEED) show that the model achieves an average classification accuracy of 90.24%, which is higher than the excellent deep learning model compared with the experiment. The built-in self-attention simple recurrent unit has stronger feature capture ability, which proves the effectiveness of the model.

Improvement of Negative Emotion Recognition in Visible Images Enhanced by Thermal Imaging

Facial expressions help in understanding the intentions of others as they are an essential means of communication, revealing human emotions. Recently, thermal imaging has been playing a complementary role in emotion recognition and is considered an alternative to overcome the drawbacks of visible imaging. Notably, a relatively severe recognition error of fear among negative emotions frequently occurs in visible imaging. This study aims to improve the recognition performance of fear by using the visible and thermal images acquired simultaneously. When fear was not recognized in a visible image, we analyzed the causes of misrecognition. We thus found the condition of replacing the image with a thermal image. It improved emotion recognition performance by 4.54% on average, compared to the performance of using only visible images. Finally, we confirmed that the thermal image effectively compensated for the visible image’s shortcomings.

Self-Management About Adjuvant Therapy in Breast Cancer Survivors: A Qualitative Study.

ObjectiveThis study aims to describe the experience and connotations of self-management about adjuvant therapy (AT) in Chinese breast cancer survivors (BCS) and explore possible factors that affect their self-management level.MethodsSemi-structured interviews were conducted in postoperative BCS undergoing AT using a qualitative description method. The conceptual structure and connotations were extracted after researchers collected and coded interview transcripts.ResultsAfter interviewing 29 patients, the conceptual framework of self-management in BCS during AT was optimized with three major themes: medical-related, emotion, and role management. Medical-related management includes AT-related adverse reactions; medical cooperation and knowledge acquisition; recovery and protection of affected limb; medical compliance; body image; sexual function and fertility; healthy lifestyle; catheter daily protection; and AT-related financial burdens. Emotion management includes recognition, prevention, and regulation of negative emotions. Role management consists of three aspects: family role, social role, and work role.ConclusionThe conceptual structure and connotations formed in this study can help researchers to evaluate and intervene in the self-management ability of BCS during AT, so as to formulate the best model of care to improve their quality of life and clinical outcomes.

Intelligent Recognition and Analysis of Negative Emotions of Undergraduates Under COVID-19.

BackgroundThe outbreak and spread of COVID-19 has brought a tremendous impact on undergraduates' study and life, and also caused anxiety, depression, fear and loneliness among undergraduates. If these individual negative emotions are not timely guided and treated, it is easy to cause the amplification of social negative emotions, resulting in individual and collective irrational behavior, and ultimately destroy social stability and trust foundation. Therefore, how to strengthen the analysis and guidance of negative emotions of undergraduates has become an important issue to be urgently solved in the training of undergraduates.MethodThis paper presents a weight and structure double-determination method. Based on this method, a Radial Basis Function Neural Networks (RBFNN) classifier is constructed for recognizing negative emotions of undergraduates. After classifying the input psychological crisis intervention scale samples by the RBFNN classifier, recognition of negative emotions for undergraduates are divided into normal, mild depression, moderate depression and severe depression.ExperimentsAfterwards, we analyze negative emotions of undergraduates and give some psychological adjustment strategies. In addition, the experiment results demonstrate that the proposed method has a good performance in terms of classification accuracy, classification time and recognition rate of negative emotions among undergraduates.

The facial emotion recognition deficit in Parkinson's disease: Implications of a visual scanning strategy.

We explored the relationship between a visual scanning strategy and a facial emotion recognition deficit in Parkinson's disease (PD). Thirty nondemented PD patients (balanced for symptom side at onset) and 20 age, education and gender-matched healthy controls (HC) were enrolled. The PD group underwent a comprehensive neuropsychological battery also exploring the executive functions. In both groups, eye movements were recorded while subjects categorized facial emotion from Ekman's 60-faces test. We were particularly interested in the location of fixations on facial pictures (top vs. bottom) and in emotional valence (positive vs. negative). We also compared performance of the two groups on a verbal emotion attribution task. Compared to HC, PD patients performed worse on visual recognition of negative emotions such as anger, fear, and sadness (where the upper part of the face is more informative than the lower part); the two groups did not differ on the verbal emotion attribution task. HC modified their visual scanning strategy (both number and overall time duration of fixations) according to the valence of the emotion; by contrast, PD showed the same pattern regardless of the valence. In the PD group, accuracy in the visual recognition of negative emotions and fixation pattern correlated with performance on tasks exploring executive functions; however, no associations were observed with severity of motor state. Our results suggest that visual scanning strategy contributes significantly to the facial emotion recognition deficit of PD patients, especially at a "high level" related to cognitive control of eye movements. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Interplay among positive and negative symptoms, neurocognition, social cognition, and functioning in clinically stable patients with schizophrenia: a network analysis

Background: Schizophrenia has a broad range of interrelated symptoms and impairment in functioning. The objective of the study was to explore the interplay between positive symptoms, negative symptoms, neurocognition, social cognition and real-life functioning in patients with schizophrenia using network analysis. Methods: Participants were 64 clinically stable patients with schizophrenia. Psychopathologic, neurocognition, social cognition, and functioning were measured using the Scale for the Assessment of Positive Symptoms, Scale for the Assessment of Negative Symptoms, Addenbrooke's Cognitive Examination III, Faces test, Reading the Mind in the Eyes test,and Personal Social Performance scale. Results: The network analysis suggested that functioning was the most central in the network followed by avolition and asociality. Functioning was directly connected to avolition, asociality, blunted affect, neurocognition and emotion recognition. The positive symptoms were the most remote and therefore the least important node. Conclusion: The high centrality of functioning suggests the need for improving of everyday life skills for patients with schizophrenia. Moreover, treatment of specific negative symptoms, neurocognition and emotion recognition could also enhance functional outcome.

Changes in functional connectivity associated with facial expression processing over the working adult lifespan

The recognition of negative emotions from facial expressions is shown to decline across the adult lifespan, with some evidence that this decline begins around middle age. While some studies have suggested ageing may be associated with changes in neural response to emotional expressions, it is not known whether ageing is associated with changes in the network connectivity associated with processing emotional expressions. In this study, we examined the effect of participant age on whole-brain connectivity to various brain regions that show connectivity during emotion processing, namely, the left and right amygdalae, medial prefrontal cortex (mPFC), and right posterior superior temporal sulcus (rpSTS). The study involved healthy participants aged 20–65 years who completed an implicit affect processing task involving facial expressions displaying anger, fear, happiness, and neutral expressions during functional magnetic resonance imaging (fMRI). Participants were also tested on recognition accuracy during an emotion labelling task. We found that participant age was negatively associated with connectivity between the left amygdala and voxels in the left occipital pole; between the rpSTS and voxels in the orbitofrontal cortex; and between the mPFC and cingulate cortex and left insular cortex. Furthermore, these effects were due to a greater age-related decline in brain connectivity for negative expressions compared to happy and neutral expressions. There was, however, no significant relationship between age and emotion recognition accuracy (though in the expected direction), and between connectivity strength and emotion recognition accuracy. Together, these results provide evidence for a specific age-related decline in the neural processing of negative emotions, and could suggest that changes in underlying network connectivity over the working adult lifespan might occur before any marked decline in the recognition of the emotion.

Interplay among positive and negative symptoms, neurocognition, social cognition, and functioning in clinically stable patients with schizophrenia: a network analysis

Background: Schizophrenia has a broad range of interrelated symptoms and impairment in functioning. The objective of the study was to explore the interplay between positive symptoms, negative symptoms, neurocognition, social cognition and functional outcome in patients with schizophrenia using network analysis. Methods: Participants were 64 clinically stable patients with schizophrenia. Psychopathologic, neurocognition, social cognition, and functional outcome were measured using the Scale for the Assessment of Positive Symptoms, Scale for the Assessment of Negative Symptoms, Addenbrooke’s Cognitive Examination III, Faces test, Reading the Mind in the Eyes test, and Personal Social Performance scale. Results: The network analysis suggested that functional outcome was the most central in the network followed by avolition and asociality. Functioning was directly connected to avolition, asociality, blunted affect, neurocognition and emotion recognition. The positive symptoms were the most remote and therefore the least important node. Conclusion: The high centrality of functioning suggests the need for improving of everyday life skills for patients with schizophrenia. Moreover, treatment of specific negative symptoms, neurocognition and emotion recognition could also enhance functional outcome.

Depressive affect moderates the effects of biological sex on the recognition of facial emotion.

Females show a small processing advantage relative to males in the ability to identify facial expressions of emotion. In laboratory studies, this is expressed as a sex difference in the accuracy of discrimination or in recognition latencies (the time required to identify an expression). Reasons for the sex difference are not well-understood. In the current pilot study, young adults (N = 62) with and without mild to moderate symptoms of depression were asked to discriminate facial images of infants and toddlers expressing six cardinal emotions. Results showed that elevated depressive affect was associated with more rapid recognition of negative emotions by females, and with potentiation of the typically observed sex difference, compared with non-depressed observers. Differences in endogenous affective status might be one proximate factor contributing to a female advantage in emotion recognition.

Interplay among positive and negative symptoms, neurocognition, social cognition, and functional outcome in clinically stable patients with schizophrenia: a network analysis.

Background: Schizophrenia has a broad range of interrelated symptoms and impairment in functioning. The objective of the study was to explore the interplay between positive symptoms, negative symptoms, neurocognition, social cognition and functional outcome in patients with schizophrenia using network analysis. Methods: Participants were 64 clinically stable patients with schizophrenia. Psychopathologic, neurocognition, social cognition, and functional outcome were measured using the Scale for the Assessment of Positive Symptoms, Scale for the Assessment of Negative Symptoms, Addenbrooke's Cognitive Examination III, Faces test, Reading the Mind in the Eyes test,and Personal Social Performance scale. Results: The network analysis suggested that functional outcome was the most central in the network followed by avolition and asociality. Functioning was directly connected to avolition, asociality, blunted affect, neurocognition and emotion recognition. The positive symptoms were the most remote and therefore the least important node. Conclusion: The high centrality of functioning suggests the need for improving of everyday life skills for patients with schizophrenia. Moreover, treatment of specific negative symptoms, neurocognition and emotion recognition could also enhance functional outcome.

Investigating the Role of Culture on Negative Emotion Expressions in the Wild.

Even though culture has been found to play some role in negative emotion expression, affective computing research primarily takes on a basic emotion approach when analyzing social signals for automatic emotion recognition technologies. Furthermore, automatic negative emotion recognition systems still train data that originates primarily from North America and contains a majority of Caucasian training samples. As such, the current study aims to address this problem by analyzing what the differences are of the underlying social signals by leveraging machine learning models to classify 3 negative emotions, contempt, anger and disgust (CAD) amongst 3 different cultures: North American, Persian, and Filipino. Using a curated data set compiled from YouTube videos, a support vector machine (SVM) was used to predict negative emotions amongst differing cultures. In addition a one-way ANOVA was used to analyse the differences that exist between each culture group in-terms of level of activation of underlying social signal. Our results not only highlighted the significant differences in the associated social signals that were activated for each culture, but also indicated the specific underlying social signals that differ in our cross-cultural data sets. Furthermore, the automatic classification methods showed North American expressions of CAD to be well-recognized, while Filipino and Persian expressions were recognized at near chance levels.