People's Emotions Research Articles

FACIAL EMOTION RECOGNITION FOR DUSKY SKINNED PEOPLE USING CNN

In computer vision, the Convolutional Neural Network is a very popular and useful model for emotion recognition. So, we are using the one of the CNN architectures to analyse the emotions called VGGNet. It is one of the methods or models of Transfer Learning Technology. It has more layers than its predecessors ImageNet and ResNet which have minimal layers when compared to VGGNet. It has 16 or 19 layers in its model to train the dataset. It can train a large dataset at a time. Our main goal is to accurately recognize the dusky skinned people's emotions. And so, for that we have used more sample images of dusky skinned people to be trained with the existing one. By doing so, we can improve the identification of emotions on dusky skinned people. And VGGNet technology also provide a great support to the improvement of the concept. Keywords: Convolutional Neural Network (CNN), VGGNet architecture, facial emotion recognition, dusky skin.

Priming through Persuasion: Towards Secure Password Behavior

Users tend to create weak passwords even for the important accounts. The prior research shed light on user's insecure password behavior, and why the interventions, including requirement specification (e.g., password composition policies) and feedback systems (e.g., password meters) fail in practice. To this end, we propose and evaluate the concept: priming-through-persuasion in the realm of secure password creation. In particular, we created visual designs, aimed at priming users about the repercussions of weak passwords before their password creation. We base our designs on two forms of persuasion methods: pathos and logos. Pathos appeals to people's emotion in order to persuade them towards an expected behavior, where logos-based rhetoric appeals to a person's sense of reason. We conducted a lab study including participatory design and semi-structured interview with 20 participants. We updated our designs in an iterative manner based on the feedback from our participants in the lab study. To evaluate our updated designs, we conducted a between-subject online study with 131 participants over Amazon Mechanical Turk. Our study provides insight into how the use of persuasion techniques contributed to user attachment and engagement with the design, as well as the comprehension of the conveyed message about password vulnerabilities. Our findings lead to the guideline for future research on leveraging the priming-through-persuasion to complement the existing techniques in encouraging users towards secure behavior.

"Internet Witch Hunt" is the Sharp Edge of the Internet Era

In the Internet era, personal freedom of speech has been fully reflected, but it has also brought a lot of negative effects."Internet witch hunting" refers to human flesh search, information collection, personal attacks on specific people on the Internet, and stirring people's emotions through the Internet. In recent years, with the development of the Internet, Internet rumors and online violence and other phenomena began to spread on the Internet. On online social media platforms, personal freedom of speech has been fully reflected, but it has also brought great hidden dangers to public security. With the development of technology, personal privacy has been paid more and more attention. How to protect personal privacy and prevent network violence has become a common problem in today's society. Starting from Python language, this paper will analyze the content of word frequency, word cloud map, theme modeling, emotion analysis results of mainstream media and social platforms for "online witch hunt" events, so as to provide a certain reference for us to better protect the personal privacy of ourselves and others.

Who is using ChatGPT and why?: extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model

Introduction. Since its public launch, ChatGPT has gained the world's attention, demonstrating the immense potential of artificial intelligence (AI). Method. To explore factors influencing the adoption of ChatGPT, we ran structural equation modelling to test the unified theory of acceptance and use of technology model while incorporating relative risk (vs. benefit) perception and emotional factors into its original form to gain a better understanding of the process. Analysis. This study utilized partial least squares–structural equation modelling (PLS-SEM) with SmartPLS4. Results. The findings revealed that in addition to individuals' technology-specific perceptions (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions), relative risk perception and emotional factors play significant roles in predicting favourable attitude and behaviour intentions towards ChatGPT. Conclusions. Our extended model fits the data well, suggesting that it is not merely a matter of convenience but also of people's reservations, expectations, and emotions toward technology, which significantly influence their willingness to adopt ChatGPT.

Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022.

As the impact of the COVID-19 pandemic winds down, both individuals and society are gradually returning to life and activities before the pandemic. This study aims to explore how people's emotions have changed from the pre-pandemic period during the pandemic to this post-emergency period and whether the sentiment level nowadays has returned to the pre-pandemic level. We collected Reddit social media data in 2019 (pre-pandemic), 2020 (peak period of the pandemic), 2021, and 2022 (late stages of the pandemic, transitioning period to the post-emergency period) from the subreddits communities in 128 universities/colleges in the U.S., and a set of school-level baseline characteristics such as location, enrollment, graduation rate, selectivity, etc. We predicted two sets of sentiments from a pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) and a graph attention network (GAT) that leverages both the rich semantic information and the relational information among posted messages and then applied model stacking to obtain the final sentiment classification. After obtaining the sentiment label for each message, we employed a generalized linear mixed-effects model to estimate the temporal trend in sentiment from 2019 to 2022 and how the school-level factors may affect the sentiment. Compared to the year 2019, the odds of negative sentiment in years 2020, 2021, and 2022 are 25%. 7.3%, and 6.3% higher, respectively, which are all statistically significant at the 5% significance level based on the multiplicity-adjusted p-values. Our study findings suggest a partial recovery in the sentiment composition (negative vs. non-negative) in the post-pandemic-emergency era. The results align with common expectations and provide a detailed quantification of how sentiments have evolved from 2019 to 2022 in the sub-population represented by the sample examined in this study.

Attitude in a crisis: A new keystone concept in crisis communication leadership

Abstract‘When people's emotions are at stake, there is the constant need to be seen to engage and connect with them in their hours of need’. This study contributes to the literature on Integrated Crisis Mapping, a stakeholder‐centric crisis communication framework that attunes organisational leaders to the emotional responses and coping needs of their primary publics. Building on Weick's insight about the importance of an attitude of wisdom in making sense of a crisis, the study proposes crisis attitude as a keystone concept to help bridge the gap between theory and practice in crisis communication. The study illustrates the concept with an attitude of humility, explaining how it could be apposite to navigating the dynamics of a crisis and serve a sensegiving function that decreases stakeholders' negative emotions and fosters their trust in leadership.

For better or for worse? Visualizing previous intensity levels improves emotion (dynamic) measurement in experience sampling.

It is a long known reality that humans have difficulty to accurately rate the absolute intensity of internal experiences, yet the predominant way experience sampling (ESM) researchers assess participants' momentary emotion levels is by means of absolute measurement scales. In a daily-life experiment (n = 178), we evaluate the efficacy of two alternative assessment methods that should solicit a simpler, relative emotional evaluation: (a) visualizing a relative anchor point on the absolute rating scale that depicts people's previous emotion rating and (b) phrasing emotion items in a relative way by asking for a comparison with earlier emotion levels, using a relative rating scale. Determining five quality criteria relevant for ESM, we conclude that a visual "Last" anchor significantly improves emotion measurement in daily life: (a) Theoretically, this method has the best perceived user experience, as people, for example, find it the easiest and most accurate way to rate their momentary emotions. Methodologically, this type of measurement generates ESM time series that (b) exhibit less measurement error, produce person-level emotion dynamic measures that are (c) often more stable, and in a few cases show stronger (d) univariate and (e) incremental relations with external criteria like neuroticism and borderline personality (e.g., emotional variability). In sum, we see value in the addition of a relative "Last" anchor to absolute measurement scales of future ESM studies on emotions, as it structures the ambiguous rating space and introduces more standardization within and between individuals. In contrast, using relatively phrased emotion items is not recommended. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dampak cyberbullying terhadap kemampuan kecerdasan emosional remaja dengan pendekatan prakiraan

The use of social media has become an inseparable part of teenagers' daily lives. Apart from having a positive impact, the existence of social media also has an impact on the negative behavior of its users, one of which is cyberbullying. This research aims to identify forms of cyberbullying among teenage victims on social media, estimate the impact of cyberbullying on teenagers' emotional intelligence abilities, and how to mitigate it. This research uses qualitative data collection techniques, including literature studies and expert interviews. The analysis uses qualitative thematics to identify forms of cyberbullying among teenage victims on social media, then analyzes predictions based on expert opinion and experience. The research results showed that there were five cases of teenage victims of cyberbullying in the form of insults and harassment on social media. Estimated impacts of these two forms of cyberbullying include teenagers having difficulty understanding and managing their own and other people's emotions, difficulty building social relationships, lack of self-confidence, stress, depression, and failure in academic achievement at school. Teenagers need to have the ability to mitigate adverse impacts; namely, first, teenagers do not need to respond to the cyberbullying they experience. Second, teenagers need support from their families and the school environment. Teenagers need to build good communication with their parents and understand how to use social media through literacy and legal risks by teachers at school. The government plays a vital role in controlling social media content that leads to cyberbullying (taking down the perpetrator's account).

베트남 호텔 종사원의 감성지능이 문화적 공감을 통해 친소비자 행동에 미치는 영향

Purpose – The purpose of this study is to explore how hotel employee emotional intelligence influences the formation of cultural empathy, and how cultural empathy affects prosocial behavior toward customers. Design/Methodology/Approach – This study conducted empirical analysis through various methodologies, including confirmatory factor analysis and structural equation modeling. For empirical analysis, a survey was conducted targeting hotel employees in Ho Chi Minh City and Da Nang, Vietnam. The study was conducted based on 261 samples. Findings – Among the sub-factors of emotional intelligence, understanding other people's emotions, controlling emotions, and utilizing emotions significantly positively affected cognitive and emotional empathy abilities. On the other hand, self-emotional understanding was found to have a positive effect only on cognitive empathy ability. This shows that the various aspects of emotional intelligence are important in forming close relationships with customers. Moreover, cultural empathy significantly impacted hotel employee prosocial behavior. Research Implications – This study reveals that self-emotion appraisal influences only cognitive empathy, suggesting a reevaluation of its role in emotional intelligence. It underscores the importance of cultural empathy in multi-cultural service environments, and recommends enhanced training programs for hotel employees to better understand and respond to customer emotional and cultural needs.

Multilingual sentiment analysis during the pandemic using deep learning models

High mood swings and increased mortality with the COVID-19 outbreak, mood changes are a concern. COVID-19 is extremely contagious, and by December 20, 2021, the global death toll from COVID-19 exceeded 5.35 million. Under such circumstances, people changed their original way of life and isolated themselves from home to work online. Most people in this situation expressed their emotions through social media. In order to understand the changes of people's emotions during the pandemic, this research analyzed the emotions of people's messages posted on social media in different countries through the SenWave dataset by different deep learning models. The dataset contains 6 languages (English, Arabic, Spanish, French, Italian and Chinese) with more than 70,000 tweets, in this paper, only tweets collected from Twitter are concerned, which contains 11 labels. The dataset will be preprocessed and transferred into numerical vector, then TextCNN, BERT, and BERT-TextCNN models are implemented for the above problems. BERT model reached 0.69 and 0.53 on precision and recall respectively on Twitter dataset.

Heterogeneous text graph for comprehensive multilingual sentiment analysis: capturing short- and long-distance semantics.

Multilingual sentiment analysis (MSA) involves the task of comprehending people's opinions, sentiments, and emotions in multilingual written texts. This task has garnered considerable attention due to its importance in extracting insights for decision-making across diverse fields such as marketing, finance, and politics. Several studies have explored MSA using deep learning methods. Nonetheless, a majority of these studies depend on sequential-based approaches, which focus on capturing short-distance semantics within adjacent word sequences, but they overlook long-distance semantics, which can provide more profound insights for analysis. In this work, we propose an approach for multilingual sentiment analysis, namely MSA-GCN, leveraging a graph convolutional network to effectively capture both short- and long-distance semantics. MSA-GCN involves the comprehensive modeling of the multilingual sentiment analysis corpus through a unified heterogeneous text graph. Subsequently, a slightly deep graph convolutional network is employed to acquire predictive representations for all nodes by encouraging the transfer learning across languages. Extensive experiments are carried out on various language combinations using different benchmark datasets to assess the efficiency of the proposed approach. These datasets include Multilingual Amazon Reviews Corpus (MARC), Internet Movie Database (IMDB), Allociné, and Muchocine. The achieved results reveal that MSA-GCN significantly outperformed all baseline models in almost all datasets with a p-value < 0.05 based on student t-test. In addition, such approach shows prominent results in a variety of language combinations, revealing the robustness of the approach against language variation.

Fake news text detection based on convolutional neural network

The swift evolution of mobile devices and multimedia technology has made the Internet one of the primary means of learning new information today. However, the huge amount of news information is often mixed with erroneous fake news, which can cause bad news events to spread and trigger people's bad emotions, putting the healthy development of society and economy at risk. Addressing the real-world application problem of swiftly and accurately detecting fake news is imperative. To mitigate the aforementioned challenges, we propose a method that uses deep learning to detect fake news and validate it through empirical studies. We begin by collecting a sizeable fake news dataset from domestic social media platforms and use a pre-trained deep learning model to extract textual features. Furthermore, we amalgamate convolutional neural networks and deep learning models to effectively glean and encompass the patterns and attributes of disinformation through an analysis of the text's semantic and structural characteristics. Finally, we experimentally evaluate the effectiveness of the method. The experimental findings demonstrate that the suggested approach exhibits commendable performance in the task of detecting fake news, effectively discerning between authentic and fabricated information. Our deep learning-based approach proves to be both efficient and highly impactful in addressing the issue of fake news within the realm of social media.

The Role of Emotional Intelligence in Leadership Effectiveness

The aim of this study is to emphasize the role that emotional intelligence plays in leadership. The ability to focus on one's own emotional state as well as the emotional signals that others are displaying in the dynamic context of an interaction is known as emotional intelligence. Keeping an eye on other people's and your own emotions during a heated discussion helps you handle that specific circumstance well. Mutual understanding is the best kind of relationship that people can have, and it happens most quickly when one is still conscious of the dynamics of emotional intelligence. The benefits that emotionally intelligent leaders provide to their followers and organisations will be examined in this study.Emotional skills can be used to manipulate other human beings in different situations. Emotional intelligence and leadership are related to each, the extent to which leadership is impacted by emotional intelligence, with the help of extensive theoretical and empirical review of literature. This paper will help to understand the different perspectives of emotional intelligence along with related components and to study the relationship between emotional intelligence and leadership. Keywords: emotional intelligence, leadership, self-awareness, self-management, social, awareness, relationship management

Анализ четырехфакторной модели симптомов аутизма у детей 3–6 лет

&lt;p&gt;The work is devoted to the empirical analysis of age shifts within the framework of the factor model of autism for preschool children. In the 2020&amp;ndash;2022 studies, we obtained a factor model of autism and identified 4 vectors of autism &amp;mdash; typical subtypes of ASD in 3&amp;ndash;4 years old children. Among them are: 1) difficulties in establishing emotional contact, expressing one's emotions and decoding other people's emotions in the context of social interaction; 2) sensory disintegration; 3) impairments in verbal and nonverbal communication and social skills; 4) hyperactivity, including excessive movements, motor disinhibition and restless behavior. It should be emphasized that the first three vectors correspond to the core symptoms of ASD in accordance with the DSM-5. While the hyperactivity factor goes beyond the main symptoms of ASD. In this paper, a comparative analysis of the severity and stability of symptoms was carried out within the framework of a 4-factor model of autism for 3&amp;ndash;4 and 5&amp;ndash;6-year-old children with autism. Sensory and communication impairments have been found to decrease with age, however, emotional impairments and hyperactivity remain at a high level. The theoretical foundations of emotional disorders and hyperactivity in children with ASD are analyzed in detail. Brief practical recommendations are presented for effective early intervention in order to improve the potential trajectory of development of preschool children with ASD.&lt;/p&gt;

Perception of Emotional Marketing Strategies in Shaping Consumer Behaviour

This kind of marketing involves using various techniques to arouse people's emotions and persuade them to buy various goods or services. Emotional advertising is a part of emotional marketing. The vital role that emotive marketing techniques play in influencing consumer behavior. This study explores the complex relationships between customer perceptions and the emotional appeal ingrained in marketing techniques, as firms increasingly understand the significance of emotional linkages in marketing. Emotions play a big role in purchasing decisions. Decisions and choices are influenced by both reasonable and emotional factors in social and professional contexts. This essay explores the relationship between consumer behavior and emotional marketing, as well as the impact that emotions have on customers' dynamic lives. Using a survey, 190 instances of different types of sentiments were randomly collected in order to understand the customers' responses to them. The questions focused on what emotions meant to them in certain situations, what kinds of emotions they experienced often, and how they felt after making a purchase. Based on the evaluation outcomes, it was discovered that emotions have a big role in customer behavior; in addition, it depends on their current mental state and past experiences. Even though it was shown that emotions affect customer behavior, the evidence was entirely emotive since everyone experiences emotions differently based on their daily interactions. Ensuring that every client's emotional requirements are met is almost impossible.

A lexicon-based diachronic comparison of emotions and sentiments in literary translation: A case study of five Chinese versions of David Copperfield.

In the Chinese context, translations have served as a useful conduit for providing access to wider literature authored in other languages. A prominent question has been whether translators' linguistic choices are influenced by factors such as translators' social and cultural background and emotions towards the texts they are translating. When multiple translations of the same text over a span of time are produced, another layer of complexity is introduced, and research such as the present study, must examine how or whether variation in the expression of emotions within translations produced over a period of time is discernible. To this end, the present study made use of Lexicon-based Sentiment Analysis (LBSA), a common natural language processing (NLP) approach, to study people's attitudes, opinions or emotions towards a certain person or thing. LBSA has attracted much attention in the literary works or translated works for analyzing reader response and appraisal of the works themselves. The present study undertook a diachronic comparison of emotions and sentiments in five translations of David Copperfield based on the emotion lexicons. The corpus of the study comprised translations of five books and 3,084,599 tokens. We applied the computational method of emotion and sentiment analysis to the emotion words in the five translations. In addition, we used python and R package to analyze the positive and negative words in five versions. The study revealed that translators as social beings in the target world express unique reactions towards the same emotion in the original text as well as in literary translations. Yet, the modern vernacular Chinese versions also showcase a similarity in the expression of emotions thus demonstrating the decisive role of the overall flow of emotion in the original plays and in translation. The contribution of the study is significant as it is a pioneering investigation given that it undertakes a sentiment and emotion analysis of literary translations in Chinese.

Emotional consequences of social debt sharing in communal relationships.

Whereas previous research has often struggled to disentangle the behavioral effects of gratitude and indebtedness felt in response to favors received by individuals, the present article clearly manifests their unique functions by investigating what happens if not just the individual but also their romantic partner is involved in a mutual exchange of favors. We propose that people in communal relationships share each other's social debt toward others that are not part of the dyad, and the emotion of indebtedness plays a vital role in that process. Three preregistered experiments revealed that people's emotions (gratitude and indebtedness) toward favors that their partners receive from others, or extend to them, are similar to when they receive or extend these favors themselves. Study 1 (N = 470) revealed that participants experienced vicarious gratitude and indebtedness to favors extended to their partners by others. Additional studies suggested that social debt between participants and third parties could be repaid by (Study 2; N = 507) and repaid to (Study 3; N = 304) their partners. These effects did not exist for exchanges in noncommunal relationships, indicating that these could not be simply attributed to indirect reciprocity. Rather, we believe that social debt sharing is a core feature of communal relationships in social exchange, and the emotion of indebtedness forms the psychological mechanism underlying this process. Crucially, indebtedness but not gratitude drove people's responses to shared debts, suggesting a unique function of indebtedness in mediating the social exchange of communal dyads. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparison of Practical Learning and Theoretical Learning a Study to Investigate Benefits and Challenges

The process of acquiring knowledge to better comprehend the various disciplines we will use in our daily lives is known as education. "Education" encompasses both the information we learn and experience outside of books and classrooms, as well as the knowledge we acquire and experience in our homes, schools, and as members of society. A specialized education modifies people's thoughts, emotions, and behaviors so they can contribute to their own well-being and improve their communities. Additionally, education prepares a person for the challenges of life by forming their character, thoughts, and interpersonal cooperation. Learning alters our outlook on life, and education is crucial for both individual and societal advancement. Currently, though, there are two ways to further your education: theoretically and practically. The advantages and difficulties of both kinds have been covered in this article.

Being Aware of Death: How and when Mortality Cues Incite Leader Expediency Versus Servant Leadership Behaviour

AbstractThe COVID‐19 crisis has been associated with existential concerns regarding mortality. These concerns, described as ‘mortality cues’, can influence people's emotions, behaviours, and the quality of leadership in organizations. Using the contingency model of death awareness (CMDA; Grant and Wade‐Benzoni, 2009), we provide new evidence on how mortality cues can incite negative and positive leadership behaviours via two forms of death awareness: death anxiety and death reflection. Specifically, we theorize that mortality cues can increase leader death anxiety, giving rise to leader expediency (a leader's use of unethical practices to expedite work for self‐serving purposes); however, mortality cues can also facilitate leader death reflection and, consequently, servant leadership behaviour. We further suggest that leaders’ responses to mortality cues depend on their psychological capital (PsyCap), such that leaders with high (vs. low) PsyCap respond to mortality cues with less expediency (via death anxiety) and more servant leader behaviours (via death reflection). We support our hypotheses through three separate studies using an experiment, time‐lagged data from healthcare workers, and daily diary data from non‐healthcare professionals. We conclude that mortality cues have a double‐edged influence on leadership behaviour. We also discuss the theoretical and practical implications of the findings.

Can Twitter data with positive or negative content affect individual emotions related to travel & tourism decisions? A study pertaining to the COVID-19 pandemic

ABSTRACT Social media significantly influences travel and tourism decisions by showcasing destinations, sharing user experiences, and influencing recommendations, ultimately shaping individuals' choices and preferences. During and after the COVID-19 pandemic, social media has influenced people's travel decisions by providing real-time updates, safety guidelines, and virtual experiences, leading to changes in travel plans and preferences. However, little attention has been paid to the effect of social media on travel and tourism-related decisions. Therefore, this paper recognizes the importance of social media and uses sentiment analysis to understand the perceptions about traveling and tourism during the pandemic. We also try to find if the effect of public tweets from social media influencers differs from ordinary people. The study uses public tweets over a one-year pandemic period, assessed by splitting the dataset into two parts, namely influencer tweets and ordinary tweets, based on the number of followers. The keywords were extracted and exposed to different groups randomly to understand the effect of the Twitter. We observe that negatively worded tweets amplified negative emotions, and positive ones increased positive emotions and impacted peoples’ travel-related decisions. Furthermore, it was ascertained that exposure to varied tweets related to pandemics significantly affects people's emotions in different groups.