The personality structure in the emotion regulation of sadness and anger

Bujor, Liliana; Turliuc, Maria Nicoleta

doi:10.1016/j.paid.2020.109999

Cited by 4 publications

(6 citation statements)

References 31 publications

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“…Khan et al [22] find correlations between Big5 personality traits and LIWC scores. The relevant LIWC categories are: work [67], tentative [68], positive emotion [48], and negative emotion [69]. Similarly, in our study, we also find a few traits have also correlations between self-efficacy and LIWC scores (Section IV) which include work [70], tentative [71], time [72], positive emotion [55], negative emotion [69] and affect [73].…”

Section: Discussionsupporting

confidence: 79%

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Mukta,

Ahmad,

Zaman

et al. 2024

IEEE Access

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General Self-Efficacy (GSE) is a vital attribute of human psychology that describes one's belief about his own ability to succeed in specific situations. GSE is composed of cognitive, social, and behavioral skills of an individual. In this research, we first develop a GSE classification model by using Facebook content (i.e., profile photos and statuses). We collect data from a total of 435 Facebook users in an ethical data collection manner. Two hybrid machine learning methods are applied based on distinct feature extraction approaches: tool-based and deep learning-based. In our tool-based approach, we employ Linguistic Inquiry and Word Count (LIWC) and Bidirectional Encoder Representations from Transformers (BERT) for text and Mediapipe and DeepFace for image feature extraction. We apply Particle Swarm Optimization (PSO) for feature selection, resulting in a robust tabular dataset with high predictive performance for GSE scores. In the deep learning-based approach, we apply BERT and 1-dimensional convolutional neural network (1D-CNN) for text feature extraction, while UNet++ handles image segmentation, and VGG16 and ResNet-152 contribute image features, fused via Canonical Correlation Analysis (CCA). We also integrate a coattention model for image and text features. Traditional machine learning models, including Random Forest (RF), Xgboost, AdaBoost, and Stacking, are then trained on the feature set to predict GSE scores. This comprehensive model showcases a multifaceted approach to GSE prediction, combining tool-based and deep learning methodologies for enhanced accuracy and insights. Then, we develop a GSE prediction model by using the mentioned tool-based (i.e., LIWC, BERT, Mediapipe, and DeepFace) and deep learning-based feature extraction methods from both image and text datasets. The tool-based model achieves remarkable accuracy percentages of 85.80% (text), 91.06% (image), and an outstanding 93.25% for the hybrid model. The deep learning-based model exhibits competitive results, with accuracies of 64.80% (text), 73.06% (image), and 81.87% for the hybrid model.

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Section: Discussionsupporting

confidence: 79%

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Mukta,

Ahmad,

Zaman

et al. 2024

IEEE Access

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“…The relationships are direct and positive in all cases, except between expressive suppression and self-efficacy for positive emotions, where the relationships are negative. Hence, emotion regulation strategies that focus on the antecedents of emotional experience enable the management of positive and negative emotions by regulating behaviour to achieve more efficient emotional coping [ 46 ]. Expressive suppression reduces self-efficacy for positive emotions [ 38 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

“…Positive emotions seem to be more strongly linked to antecedent-focused strategies in emotional experience. One possible explanation is that people with high emotional stability feel that negative emotional coping is an achievement and pay less attention to positive emotional coping [ 45 , 46 ]. The results partially confirm Hypothesis 4 and are consistent with earlier research, although some studies have considered neuroticism instead of emotional stability (given that they are opposites) [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the way in which self-efficacy is evaluated is important in understanding the process of emotion regulation [ 44 , 45 ]. Bujor and Turliuc [ 46 ] found that, in addition to reducing the intensity of negative emotions, cognitive reappraisal also boosted positive emotions, whereas expressive suppression could be an efficient way of regulating the expression but not the experience of emotions. High scores in expressive suppression are associated with greater negative affect and lower positive affect [ 38 , 47 , 48 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, high levels of emotional stability are related to self-efficacy for negative as well as positive emotions [ 51 ]. People with high emotional stability tend to experience negative emotions of anger, sadness, and emotional distress at lower levels than those with low emotional stability [ 46 ]. Furthermore, emotional stability is positively correlated with self-efficacy for the negative emotions of despair and distress [ 52 ].…”

Section: Introductionmentioning

confidence: 99%

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Emotion Regulation and Self-Efficacy: The Mediating Role of Emotional Stability and Extraversion in Adolescence

Doménech,

Tur-Porcar,

Mestre-Escrivá

2024

Behavioral Sciences

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The feeling of emotional self-efficacy helps people understand how to handle positive and negative emotions. Emotion regulation is the process that helps people control their emotions so that they can adapt to the demands of the environment. This study has a twofold aim. First, it examines the relationships among emotion regulation, the personality traits of extraversion and emotional stability, and the feeling of emotional self-efficacy for positive and negative emotions in an adolescent population. Second, it examines the mediating role of personality traits (extraversion and emotional stability) in the relationship between emotion regulation and emotional self-efficacy for positive and negative emotions. The participants were 703 adolescents (49.9% male and 50.1% female) aged between 15 and 18 years (M = 15.86, SD = 0.30). Significant relationships were observed among emotion regulation, the personality traits of extraversion and emotional stability, and emotional self-efficacy for positive and negative emotions. The structural equation model confirmed the direct link between emotion regulation and emotional self-efficacy and mediation by the personality traits of extraversion and emotional stability. This study confirms that emotional self-efficacy is connected to the emotion regulation strategies that adolescents use. Effective emotion regulation encourages self-perception and emotional coping. The results are discussed in connection to previous research.

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Disgust and Emotion Dysregulation in Misophonia: a Case for Mental Contamination?

Barahmand

Stalias-Mantzikos

Rotlevi

et al. 2021

Int J Ment Health Addiction

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The personality structure in the emotion regulation of sadness and anger

Cited by 4 publications

References 31 publications

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Emotion Regulation and Self-Efficacy: The Mediating Role of Emotional Stability and Extraversion in Adolescence

Disgust and Emotion Dysregulation in Misophonia: a Case for Mental Contamination?

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