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Empathy and compassion as fundamental elements of social cognition
This investigation into compassion and empathy highlights their crucial functions in social cognition, which influence engagements in various settings. Cultural dimensions underscore the significance of human connection by highlighting the societal influences that shape empathetic behaviours. The correlation between compassion, empathy, and mental health underscores their capacity to cultivate resilience. They make valuable contributions to communication and conflict resolution within interpersonal relationships. Efficacious interventions provide opportunities for individual development. Ethical considerations emphasize the importance of maintaining a delicate equilibrium between self-care and empathy. Ongoing technological and neurological research promises an expansion of applications. Cultivating kindness and compassion revolutionizes societies, ushering in an era of more significant global interdependence where mutual comprehension underpins all human engagements. 2024, IGI Global. -
Emotional needs of women post-rescue from sex trafficking in India
Sex trafficking has persisted a social crime that maintains its status despite being unlawful. Since it prevails, there is a need to investigate it to understand the effects and consequences of the same on the survivors. The current study aims to understand the emotional needs of survivors post-rescue from sex trafficking living in aftercare homes in India and to look into survivors suggestions post-rescue to NGOs, society, family, government and police. It included ten survivors from sex trafficking, ages between 18 to 24years old. They are emerging adults who have experienced sex trafficking for at least one year, regardless of whether trafficking happened in childhood, adolescence or early adulthood, rescued one to five years ago. The researcher used a phenomenological approach. Thematic analysis was employed to identify themes within the data collected from the participants. Findings revealed that survivors had got a better life after the rescue, and they need acceptance, respect, understanding, and they need to develop trust on people around them. They still have many challenges post-rescue such as lack of education and job opportunities. They need guidance to start a new life. Mostly, sex trafficking survivors need safety and protection. 2019, 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Emotional Maturity and Self-Perception Among Adolescents Living With HIVNeed for Life-Skills Intervention
Among Adolescents Living With HIV (hereby referred to as ALWH), emotional maturity and self-perception gain importance because they play a decisive role in the overall development into adulthood. This study examines the relationship between self-perception and emotional maturity among ALWH. The sample comprised of 92 male and female ALWH, aged 13 to 18 years. Self-Perception Questionnaire and Emotional Maturity Scale were the tools used. Descriptive and inferential statistics were used for analysis. Correlation analysis showed a negative moderate relationship between Physical Competence and Global Self-Worth dimensions of self-perception and emotional maturity, with regression analysis confirming their predictive abilities. Discussions are focused on the relevance of the findings in terms of a need for a psychosocial life-skills intervention for the population. The Author(s) 2019. -
Emotional Landscape of Social Media: Exploring Sentiment Patterns
Sentiment analysis, a pivotal research area, involves exploring emotions, attitudes, and evaluations prevalent in diverse public spheres. In the contemporary era, individuals extensively share their perspectives on various subjects through social media platforms. Twitter has emerged as a prominent microblogging site, facilitating users to express opinions and insights globally. However, disrespectful or unfair comments have prompted specific platforms to restrict user comments, highlighting the need to foster productive discourse on social media. This study addresses this imperative by analyzing sentiments using data from Twitter. This work employed various deep learning algorithms and methods to classify elements as negative or positive. The Sentiment140 dataset, sourced from Twitter, serves as the training data for the models to identify the most accurate classification approach. By delving into sentiment analysis on Twitter, the study contributes to a better understanding of the nuances of online expressions. It aims to enhance the overall quality of discourse in social media. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Emotional intelligence, job satisfaction and psychological well-being among nurses in a tertiary care hospital
Background: Emotional intelligence helps in preservation of mental health because of their effective emotional regulation skills. Objectives: We aimed to evaluate the impact of emotional intelligence on nurses job satisfaction and psychological well-being. Methods: This cross-sectional study was conducted in a tertiary hospital and included 120 nurses. Wong and Law Emotional Intelligence Scale, Psychological General Well-being scale and Job Satisfaction Survey questionnaires were used. Results: The study showed a low positive correlation between emotional intelligence and psychological wellbeing (r=0.313) and a low correlation between emotional intelligence and job satisfaction (r= 0.122). The emotional intelligence was significantly correlated to their psychological well-being (9.8%). Conclusion: Nurses with higher emotional intelligence experience greater psychological well-being. We did not find a link between emotional intelligence and job satisfaction. Implementing interventions to enhance emotional intelligence in nurses is crucial for improving psychological well-being and reducing burnout risk. The Author(s). 2024. -
Emotional Intelligence as a Predictor of Police Operational Stress: A Pilot Study
The present study examined the relationship between police operational stress and emotional intelligence. The study also observed the difference in operational stress and emotional intelligence concerning gender, rank, education, and marital status. The sample included 80 police officers from Bangalore, India. The operational police stress questionnaire developed by McCreary and Thompson (2006) and emotional intelligence scale developed by Hyde et al. (2002) were used to measure police operational stress and emotional intelligence, respectively. Independent sample t-test and Cohens d indicated that differences in gender, rank, education, and marital status had no significant effect on police operational stress. Gender differences had a significant effect on the emotional intelligence factors, empathy, and self-motivation. Differences in rank had a significant effect on empathy, self-motivation, emotional stability, managing relationships, integrity, value orientation, and commitment. Differences in marital status had a significant effect on value orientation. Correlation analysis showed that operational stress had a significant negative relationship with emotional intelligence and its factors such as self-motivation, emotional stability, value orientation, and altruistic behavior. Regression analysis showed emotional intelligence and its factor, emotional stability, as significant predictors of police operational stress. 2021, Society for Police and Criminal Psychology. -
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Emotional intelligence and work life balance of women IT professional in Bangalore /
Adarsh Journal of Management Research, Vol.7, Issue 2, pp.241-253, ISSN No: 0974-7028. -
Emotional intelligence and self esteem in cannabis abusers
This study was taken up to investigate emotional intelligence and self esteem in cannabis abusers. Cross sectional hospital based study, Study is based on a sample of 200 individuals. 100 Cannabis dependent, diagnosed based on DSM-IV TR was selected from two different hospitals in north India. 100 healthy matched subjects constituted the control group. Assessment was done using MINI, General Health Questionnaire, Indian Adaptation of Emotional Intelligence Scale and Rosenberg Self Esteem Scale. Significant differences were seen in Emotional Intelligence between the Cannabis dependent group and normal control group. The cannabis dependent groups scored significantly low on emotional intelligence in comparison with control group. Further, cannabis dependent group scored significantly lower on score of self esteem than the normal control group. Relationship between emotional intelligence and self esteem was found to be positively correlated. Our study suggests an association between low emotional intelligence, low self esteem and cannabis dependence and the prevention and treatment of cannabis dependence should lay focus on these factors. Journal of the Indian Academy of Applied Psychology. -
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Emotional Intelligence and General Well-Being Among Middle Aged People
International Journal of Research in Social Sciences, Vol-2 (4), pp. 454-471. ISSN-2249-2496 -
Emotional Intelligence and Cross-Cultural Adaptation of Indian Students in the Context of Interstate Education
India is known for its cultural diversity based on several factors, such as language, religion, race, and customs. In India, people used to move from one place to another for various purposes, and this was particularly the case with students in pursuit of education. In such situations, cross-cultural adaptation is one of the factors that facilitate their adjustment to new cultures and surroundings. Cross-cultural adaptation is needed when a person has to live in a different cultural setting than their own native place. Being sensitive to others emotions is essential when one lives in a new place. Emotional intelligence helps in that way and influences cross-cultural adaptation. Therefore, the present study was intended to explore the influence of emotional intelligence on cross-cultural adaptation. As many as 332 students, aged 17 to 29, who moved to another state for education, participated in the study. Emotional Intelligence Scale and Cross-Cultural Adjustment Scale were used for data collection. The components of emotional intelligence, such as self-emotional appraisal (SEA) and others emotional appraisal (OEA), were found to significantly influence expatriate adjustment. Furthermore, SEA and OEA have also influenced cultural novelty and the use of emotions (UOE). Students from rural areas were found to have more cross-cultural adaptation in the presence of their friends company compared to urban dwellers. In summary, the current study emphasizes the importance of higher emotional intelligence for better cross-cultural adaptation. 2025 Common Ground Research Networks. All rights reserved. -
Emotional Inhibition and Personality as Predictors of Anxiety and Depression in Young Adults
Purpose: Anxiety and depression have been major contributors to the global burden of disease, and the impact has been exacerbated following the COVID-19 pandemic. Therefore, the aim of this study was to understand the association between emotional suppression and the introverted-extraverted dimension of personality in young people and anxiety and depression. Method: Participants were 152 Indian females between the age group of 18-25 years who provided basic demographic details and completed three questionnaires via a google form. Findings: Results described a significant negative correlation of anxiety r (152) = .500, p <0.01and depression r(152)=.471, p <0.01 with emotional inhibition. There was also a significant positive correlation of anxiety r (152) = .288,p < 0.01 and depression r(152)= .288, p <0.01 with personality. While Emotional inhibition emerged as a significant negative predictor of anxiety (R2= .250) as well as of depression (R2=.222), personality (R2=.243) emerged as a significant predictor of depression. Conclusion/Value: Contrary to popular belief, the results of this study suggest that anxiety and depression are inversely related to emotional inhibition. It restores the complexity of emotions and the need to investigate their role in various pathologies. These findings provide an initial basis for further investigation into the role of emotional expression and suppression in the Indian population. 2024 RJ4All. -
Emotional Abuse and the Pandemic in India: Implications for Policy, Research, and Practice
During the COVID-19 outbreak, cases of violence and abuse have increased significantly around the world, necessitating a reevaluation of our relation-ships. Both violence and abuse seek to control and instill fear in the individ-ual, gradually disrupting their overall well-being. Emotional abuse does not receive the same level of attention and social response as other forms of abuse due to its subtle nature. Its effects are as harmful as physical and sexual abuse, with serious consequences for the mental health of individual and their families. The COVID-19 pandemic has brought to light the importance of mental health. With the imposition of lockdown in India, the number of helplines for domestic violence and abuse has skyrocketed. Abuse experien-ces can be seen to be bidirectional; women are not alone in such instances. Many cases, however, go unreported and never reach formal institutions. The National Family Health Survey (2019-2021) reveals the current state of Indian health and nutrition, but emotional abuse (also referred to inter-changeably in this article as emotional violence) only includes responses from women and is no longer included under spousal violence in the most recent edition. This article also includes recommendations and attempts to highlight existing shortcomings in addressing the issue of emotional violence. The articles cited in this article were obtained from electronic databases. Other secondary data sources mentioned include newspaper articles, magazines, census reports, and periodicals. 2024 Springer Publishing Company. -
Emotion Regulation and Psychological Well-being as Contributors Towards Mindfulness Among Under-Graduate Students
Emotion regulation is generally described as the ability of an individual not only to manage emotions effectively but also to respond effectively to the emotional experience. It has also been viewed as a crucial aspect for psychological well-being. It is a psychological state which means more than just being free from stress and not having any other psychological disorder reported by the individual. At the same time, students with higher emotion regulation and psychological well-being are expected to be more attentive and able to observe, describe and participate in the present moment, effectively, with non-judgmental awareness, which is in turn defined as mindfulness. Hence, it has been expected that participants with higher emotion regulation and psychological well-being would also report higher levels of mindfulness. Therefore, the present empirical investigation has been conducted with an objective of assessing the level of emotion regulation, psychological well-being and mindfulness among under-graduate students. Additionally, it was also expected that all the said variables would be positively correlated and emotion regulation and psychological well-being would predict mindfulness positively for under-graduate students. For this purpose, ex post facto research design was adopted, and standardized tools pertaining to emotion regulation, psychological well-being and mindfulness were administered on a sample of 104 under-graduate students. The results of correlation statistics revealed that emotional regulation (r=0.27; p<0.01) and psychological well-being (r=0.21; p<0.01) are the positive and significant correlates of mindfulness. Additionally, statistical outcomes of stepwise multiple regression analysis confirm that emotion regulation and psychological well-being are the significant predictors of mindfulness and contribute collectively towards a 11% variance towards the same. 2020, Springer Nature Switzerland AG. -
Emotion Detection Using Machine Learning Technique
Face Emotion Recognition (FER) is an emerging and crucial topic today; since much research has been done in this field, there are still many things to explore. In daily life, where people dont have time to fill out feedback, emotion detection plays an important role, which helps to know customer feedback by analyzing expressions and gestures. Analyzing current studies in emotion recognition demonstrates notable advancements made possible by deep learning. A thorough overview of facial emotion recognition (FER) is provided in this publication. The literature cited in this study is taken from various credible research published in the last 10years. This study has built a model for emotion recognition using photos or a camera. The paper is based on the concepts of Machine Learning (ML), Deep Learning (DL), and Neural Networks (NN). A range of publicly available datasets have been used to evaluate evaluation metrics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
EMOTICONS AND THE NON-VERBAL COMMUNICATION: WITH REFERENCE TO FACEBOOK
In the recent years, the use of emoticons in text-based and computer-mediated communications has gained a lot of popularity. Though emoticons (a combination of punctuation marks and letters) first began as a representation of facial expression, they have over the years been transformed to now include graphical representations of a variety of items (both static and animated). The usage of emoticons and their interpretation differ from one person to another, depending on factors such as gender, age and culture. Facebook is a platform where people across the globe communicate, share opinions and connect with each other. The researcher, thus, seeks to understand whether emoticons have the ability to infuse the text-based computer-mediated- communications on Facebook with the richness and authenticity of face-to-face interactions, and to arrive at an understanding of how these different groups use and interpret emoticons. A sample size of 139 was selected using the snowball sampling technique. The methods of primary data collection included surveys in the form of questionnaires that were distributed online. A quantitative analysis of the collected data was conducted using SPSS. The study revealed that age, gender and location do have a bearing on the patterns of usage and interpretation of emoticons. It also showed that emoticons cannot provide the text-based computer-mediated- communications on Facebook with the richness and authenticity of face-to-face interactions. -
EMONET: A Cross Database Progressive Deep Network for Facial Expression Recognition
Recognizing facial features to detect emotions has always been an interesting topic for research in the field of Computer vision and cognitive emotional analysis. In this research a model to detect and classify emotions is explored, using Deep Convolutional Neural Networks (DCNN). This model intends to classify the primary emotions (Anger, Disgust, Fear, Happy, Sad, Surprise and Neutral) using progressive learning model for a Facial Expression Recognition (FER) System. The proposed model (EmoNet) is developed based on a linear growing-shrinking filter method that shows prominent extraction of robust features for learning and interprets emotional classification for an improved accuracy. EmoNet incorporates Progressive- Resizing (PR) of images to accommodate improved learning traits from emotional datasets by adding more image data for training and Validation which helped in improving the model's accuracy by 5%. Cross validations were carried out on the model, this enabled the model to be ready for testing on new data. EmoNet results signifies improved performance with respect to accuracy, precision and recall due to the incorporation of progressive learning Framework, Tuning Hyper parameters of the network, Image Augmentation and moderating generalization and Bias on the images. These parameters are compared with the existing models of Emotional analysis with the various datasets that are prominently available for research. The Methods, Image Data and the Fine-tuned model combinedly contributed in achieving 83.6%, 78.4%, 98.1% and 99.5% on FER2013, IMFDB, CK+ and JAFFE respectively. EmoNet has worked on four different datasets and achieved an overall accuracy of 90%. 2020. All Rights Reserved. -
Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning
Analyzing the sentiment behind emojis can provide valuable insights into the emotional context and user sentiment associated with textual content. To conduct a comparative analysis of diverse supervised machine learning models that can achieve the highest level of accuracy in Emoji Sentiment Analysis is the purpose of this research. Five machine learning models used in this research are K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Logistic Regression, Naive Bayes, and Random Forest. The experimental process resulted in ANN and KNN models giving an accuracy of 92%. The ANN model shows its proficiency in effectively managing large datasets. ANN also supports fault tolerance. The KNN model refrains from conducting calculations during the training phase and only constructs a model when a query is executed on the dataset. This characteristic makes KNN particularly well-suited for data mining. Both ANN and K-NN excelled in the experimental study due to these distinctive attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Emission line star catalogues post- Gaia DR3: A validation of Gaia DR3 data using the LAMOST OBA emission catalogue
Aims.Gaia Data Release 3 (DR3) and further releases have the potential to identify and categorise new emission-line stars in the Galaxy. We perform a comprehensive validation of astrophysical parameters from Gaia DR3 with the spectroscopically estimated emission-line star parameters from the LAMOST OBA emission catalogue. Method. We compare different astrophysical parameters provided by Gaia DR3 with those estimated using LAMOST spectra. By using a larger sample of emission-line stars, we performed a global polynomial and piece-wise linear fit to update the empirical relation to convert the Gaia DR3 pseudo-equivalent width to the observed equivalent width, after removing the weak emitters from the analysis. Results. We find that the emission-line source classifications given by DR3 is in reasonable agreement with the classification from the LAMOST OBA emission catalogue. The astrophysical parameters estimated by the esphs module from Gaia DR3 provides a better estimate when compared to gspphot and gspspec. A second degree polynomial relation is provided along with piece-wise linear fit parameters for the equivalent width conversion. We notice that the LAMOST stars with weak H? emission are not identified to be in emission from BP/RP spectra. This suggests that emission-line sources identified by Gaia DR3 are incomplete. In addition, Gaia DR3 provides valuable information about the binary and variable nature of a sample of emission-line stars. 2022 EDP Sciences. All rights reserved.