Types Of Emotions Research Articles

A latent profile analysis of L2 writing emotions and their relations to writing buoyancy, motivation and proficiency

Abstract Second/foreign language (L2) writing emotions play an important role in language writing outcomes. However, extant literature was dominated by L2 anxiety with other types of emotions being neglected. Further, little is known about whether English-as-a-foreign-language (EFL) writers demonstrate heterogenous patterns of emotional experiences in English writing. The aim of the study was to identify intra-individual differences in EFL writing emotion patterns and how these patterns differed from each other in terms of writing buoyancy, motivation, and proficiency. Through convenience sampling, three hundred and sixty-three EFL undergraduates in China were recruited and they completed a battery of questionnaires. Latent profile analysis (LPA) revealed a three-profile solution. They were labelled as the “positive type” (PT), “negative type” (NT) and “moderate type” (MT). Three groups reported significantly different levels of writing buoyancy and motivation with the highest scores of them being found among the PT group, followed by MT, and lastly NT. The writing proficiency was higher for the PT group than for the NT group, but the PT group did not differ from the NT or MT group. The dominance of MT group suggested that most students exhibited mild attitudes toward EFL writing. The distinct patterns of EFL writing emotions and their influences on writing outcomes suggested that teachers should boost students’ emotional learning competence and adjust the teaching approach accordingly.

Exploring the sentimental features of rumor messages and investors' intentions to invest

Investors' decisions to invest in stock markets can be significantly influenced by online rumors generated by certain companies or influencers. The current understanding of how certain sentimental features can help increase the prediction capabilities of online rumors is still in its fancy stage. This study explored the types of topics and emotions found in rumor messages and how they are associated with investors' decisions to invest in stocks. We also investigated the potential of using these emotions in predicting investors' intention to invest in stock markets. The sentimental features consisted of users' emotions (anger, fear, sadness, joy, and trust) and polarity (positive, negative, and neutral). A topic modeling approach was applied to identify logical associations between different sentimental features of rumors on Twitter. The results showed that rumors tweets associated with investors' intention to invest were linked to the joy and trust sentiments, while the anger and fear sentiments were linked to no intention to invest. The results showed that these emotions can be used in predicting the impact of online rumors on investors’ investment decisions. The prediction model can be useful for stock market prediction by enabling managers and researchers to analyze and assess the magnitudinal impact of rumors on certain investment decisions. The outcomes can also help decision and policy makers to take the required actions to prevent possible financial instability due to COVID-19 or other future events.

From pandemic to Plandemic: Examining the amplification and attenuation of COVID-19 misinformation on social media

This study examines the proliferation of COVID-19 misinformation through Plandemic—a pseudo-documentary of COVID-19 conspiracy theories—on social media and examines how factors such as (a) themes of misinformation, (b) types of misinformation, (c) sources of misinformation, (d) emotions of misinformation, and (e) fact-checking labels amplify or attenuate online misinformation during the early days of the pandemic. Using CrowdTangle, a Facebook API, we collected a total of 5732 publicly available Facebook pages posts containing Plandemic-related keywords from January 1 to December 19, 2020. A random sample of 600 posts was subsequently coded, and the data were analyzed using negative binomial regression to examine factors associated with amplification and attenuation. Overall, the extended an extended Social Amplification of Risk Framework (SARF) provided a theoretical lens to understand why certain misinformation was amplified, while others were attenuated. As for posts with misinformation, results showed that themes related to private firms, treatment and prevention of virus transmission, diagnosis and health impacts, virus origins, and social impact were more likely to be amplified. While the different types of misinformation (manipulated, fabricated, or satire) and emotions were not associated with amplification, the type of fact-check labels did influence the virality of misinformation. Specifically, posts that were flagged as false by Facebook were more likely to be amplified, while the virality of posts flagged as containing partially false information was attenuated. Theoretical and practical implications were discussed.

Jealousy in Men and Women in Couples Infidelity through Online Media

Every human being in this world has a jealous attitude that has been felt by both men and women. In the younger generation which is a generation in the early stages of maturity jealousy often occurs especially for young couples who are in a dating relationship. Jealousy usually occurs due to a partner affair done through online media or the use of the internet as a medium to communicate with third people. This study aims to find out the extent to which differences in jealousy in men and women that are consistently found in real life can also be found in modern life which involves the use of internet technology. The method used in this research is a quantitative research method. This research was conducted in Indonesia by involving young people in it. In taking the research sample, several characteristics of the participants had to be met, including participants aged 20 to 40 years, single, had at least a high school education, and used the internet for at least 7 hours per week. The results showed that there was a significant difference in female participants in jealousy facing emotional and sexual types of infidelity via the internet and vice versa. In addition, the majority of people in Indonesia, especially young people or students in Indonesia, use social media, and jealousy between partners is often expressed on social media, and young people in Indonesia are easily influenced by the times

Feeling Politics at High School: Antecedents and Effects of Emotions in Civic Education

Studies on voters have shown that emotions are an important factor in politics. However, less is known about emotions experienced by adolescents in the context of civic education. Applying the control‐value theory to a cross‐curricular context, we analyze the relations between two types of emotions experienced in civic education and their antecedents and effects on learning: achievement emotions experienced during in‐class political discussions and epistemic emotions experienced during individual processing of political information in school. We hypothesized both types of emotions relate to control and value appraisal antecedents (e.g., internal political efficacy, personal importance). Further, we expected—depending on the specific emotion—positive or negative relations with academic outcomes (e.g., motivation, engagement, and knowledge). Data were collected with an online questionnaire in Austrian upper secondary schools with students (N = 594, Mage = 17.1) from grades 10 to 13. Bivariate latent correlations broadly support the expectations: We found mainly positive associations of antecedents and outcomes to enjoyment, hope, pride, curiosity, and surprise and negative associations to hopelessness, confusion, and boredom, but more complex correlation patterns for anxiety, anger, and shame. The results provide first insights into which emotions are experienced during political learning and underscore the importance of acknowledging the role of emotions in civic education.

Multi-level graded facial emotion intensity recognition using MCANN for health care

Facial emotion recognition analysis is widely used in various social fields, including Law Enforcement for police interrogation, virtual assistants, hospitals for understanding patients’ expressions, etc. In the field of medical treatment such as psychologically affected patients, patients undergoing difficult surgeries, etc require emotional recognition in real-time. The current emotional analysis employs interest points as landmarks in facial images affected by a few emotions Many researchers have proposed 7 different types of emotions (amusement, anger, disgust, fear, and sadness). In our work, we propose a deep learning-based multi-level graded facial emotions of 21 different types with our proposed facial emotional feature extraction technique called as Deep Facial Action Extraction Units (DFAEU). Then using our Multi-Class Artificial Neural Network (MCANN) architecture the model is trained to classify different emotions. The proposed method makes use of VGG-16 for the analysis of emotion grades. The performance of our model is evaluated using two algorithms Sparse Batch Normalization CNN (SBN-CNN) and CNN with Attention mechanism (ACNN) along with datasets Facial Emotion Recognition Challenge (FERC-2013). Our model outperforms 86.34 percent and 98.6 percent precision.

EEG rhythm based emotion recognition using multivariate decomposition and ensemble machine learning classifier

Recently, electroencephalogram (EEG) signals have shown great potential to recognize human emotions. The goal of effective computing is to assist computers in understanding various types of emotions via human–computer interaction (HCI). Multichannel EEG signals are used to measure the electrical activity of the brain in space and time. Automated emotion recognition using multichannel EEG signals is an interesting area of cognitive neuroscience and affective computing research. This research proposes EEG multichannel rhythmic features and ensemble machine learning (EML) classifiers with leave-one-subject-out cross-validation (LOSOCV) for automatic emotion classification from multichannel EEG recordings. Multivariate fast iterative filtering (MvFIF) is used to assess the EEG rhythm sequences. EEG rhythms delta(δ), theta(θ), alpha(α), beta(β), and gamma(γ) are separated based on the mean frequency of the EEG rhythm sequence. Three Hjorth parameters and nine entropy features were extracted from multichannel EEG rhythms. Extracted features are selected using the minimum redundancy maximum relevance (mRMR) approach. The experimental design was performed on two emotional datasets (GAMEEMO and DREAMER). The validation showed that gamma rhythm multichannel features with EML-based subspace K-nearest neighbor (SS KNN) were as high as 93.5%–99.8%, achieving high classification accuracy. The comparisons of δ, θ, α, β, and γ rhythms with EML, support vector machine (SVM), and artificial neural network (ANN) were performed. we also analyzed multi-class emotions (HVHA, HVLA, LVHA, LVLA) with an ensemble-based bagging tree on gamma rhythm. It provides a novel solution for multichannel rhythm-specific features in EEG data analysis.

IMPROVED AUTOMATIC LIP-READING BASED ON THE EVALUATION OF INTENSITY LEVEL OF SPEAKER’S EMOTION

Abstract. Automatic audio-visual speech recognition systems (AVSRs) have recently achieved tremendous success, especially in limited vocabulary tasks by far surpassing human abilities to recognize speech, especially in acoustically noisy conditions. Automatic speech recognition systems based on processing of audio and video information are being actively researched and developed all over the world. However, scientific studies aimed at analyzing the influence of the speaker's emotional state (anger, disgust, fear, happy, neutral, and sad), and, most importantly, intensity level of emotion (low - LO, medium - MD, high - HI) on automatic lip-reading have not been conducted. In this regard, the relevance of this research topic cannot be overestimated and requires detailed study. In this paper, we present a novel approach for emotional speech lip-reading, that includes evaluation of a speaker’s emotion and its intensity level. The proposed approach uses visual speech data to detect a person’s emotion type and its intensity level and based on this information assigns it to one of the trained emotional lip-reading models. This essentially resolves the multi-emotional lip-reading issue associated with most real-life scenarios. The proposed approach improves the state-of-the-art results due to the consideration of the intensity of the pronounced audio-visual speech up to 8.2% in terms of the accuracy. Current research is the first step in the creation of emotion-robust speech recognition systems and leaves open a wide field for further research.

Populism and emotions: a comparative study using Machine Learning

AbstractThis study aims to unpack the mobilization of emotions in the political discourse of populist and non-populist parties and above all, across ‘varieties of populism’ (right wing vs. left wing or hybrid). Is there an empirical connection between emotions and populism? Are all types of populisms alike with regards to the emotional appeals within their political discourse? Focusing on Italy as a crucial case for populist communication and using a novel methodological approach based on supervised machine learning, it systematically investigates the intensity and trends of specific emotions in political discourses (institutional and informal, i.e. leaders’ speeches) of all Italian political parties over the last 20 years, for a corpus of more than 13,000 sentences analysed. The findings confirm that (i) populists tend to use more (and a broader repertoire of) emotional appeals than non-populist parties; however (ii) overall, there is an increase in the use of these appeals in the Italian political party discourse over time, especially in terms of negative emotions; and, most importantly, (iii) different types of emotions are mobilized by different types of populisms. Right wing populism mainly uses negative emotions while left wing or hybrid populism employs positive emotional appeals. The communication arena (party manifestoes vs. speeches) nevertheless does matter in the degree and types of emotions mobilized by political actors. This study identifies important implications for research on emotional appeals in politics, populist communication and political campaigning, and populist contagion from an emotion-based perspective.

Initial evidence for a systematic link between core values and emotional experiences in environmental situations

While literature in environmental psychology has generally treated values and emotions as separate constructs, research has pointed out that value endorsement and emotional experience are profoundly interconnected processes. Recent studies have shown that the endorsement of environmental values predicted the intensity of the emotions that people experienced towards nature and climate change. Building on these findings, this study investigates whether environmental values may have a role not only in determining the intensity, but also in differentiating the quality of the emotional experience in environmentally-relevant situations. Results showed that in environmentally relevant situations higher centrality of self-transcendence values was selectively associated with the experience of affiliation emotions (i.e., emotions concerning one's relationship with the “other”), while higher activation of self-enhancement values was selectively associated to the experience of both affiliation and achievement (i.e., emotions concerning one's needs and status) positive emotions. These findings advance existing literature by providing first evidence for a systematic link between values and types of emotions experienced in environmentally-relevant situations.

Social Anxiety Symptoms Predict Poorer Facial Emotion Recognition in Autistic Male Adolescents and Young Adults Without Intellectual Disability.

Utilizing a novel computerized task, we aimed to examine whether social anxiety symptoms would be related to individual differences in facial emotion recognition (FER) in a sample of autistic male adolescents and young adults without intellectual disability. Results indicated that social anxiety and IQ predicted poorer FER, irrespective of specific emotion type. When probing specific effects within emotion and condition types, social anxiety impacted surprise and disgust FER during a truncated viewing condition and not full viewing condition. Collectively, results suggest that social anxiety in autism may play a larger role in FER than previously thought. Future work should consider the role of social anxiety within autism as a factor that may meaningfully relate to FER assessment and intervention.

Dynamics of Academic Emotions of Pre-service Teacher Professional Education Students

Academic emotion is a psychological condition experienced by students before, during, and after carrying out academically demanding activities. The academic emotions experienced by PPG students are divided into positive and negative. This study aims to determine the academic emotions of Pre-service PPG students in terms of gender, origin from PT, and regional origin and find out the dynamics of academic emotions seen from the causes and effects of the emotions that occur. This research uses descriptive quantitative using the Achievement Emotions Questionnaire (AEQ) instrument developed by Pekrun, which is modified into 62-item statements that measure groups of academic emotions (positive-activation, negative-activation, and negative-deactivation) and observation. The instrument was validated using confirmatory factor analysis and reliability tests on JASP. The research sample was Pre-service PPG Students in the Guidance and Counseling study field, totaling 36 people. Data analysis used descriptive analysis and ANOVA. The study results show that students' academic emotions are high. PPG students have types of positive emotions and negative emotions. In the gender aspect, female students show negative emotions more quickly than men and positive ones. However, in the aspect of regional origin and PT, there is no significant difference. The positive and negative emotions experienced by students are caused by many internal and external factors that affect academic success in undergoing Pre-service PPG

The emotional side of taking part in a cancer clinical trial.

Taking part in a cancer clinical trial often represents a source of psychological distress and emotional activation among patients and their caregivers. Nowadays, social media platforms provide a space for these groups to freely express and share their emotional experiences. We aimed to reveal the most prevalent basic and complex emotions and sentiments in the posts of the patients and caregivers contemplating clinical trials on Reddit. Additionally, we aimed to categorize the types of users and posts. With the use of keywords referring to clinical trials, we searched for public posts on the subreddit 'cancer'. R studio v. 4.1.2 (2021-11-01) and NRC Emotion Lexicon was used for analysis. Following the theoretical framework of Plutchik's wheel of emotions, the analysis included: 8 basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and 4 types of complex emotions (primary, secondary, tertiary, and opposite dyads). We utilized the package 'PyPlutchik' to visualize the emotion wheels in Python 3.10.5. A total of 241 posts were included in the final database. User types (129 patients, 112 caregivers) and post types (142 expressed shared experience, 77 expressed advice, and 85 conveyed both) were identified. Both positive (N = 2557, M = .68) and negative (N = 2154, M = .57) sentiments were high. The most prevalent basic emotions were: fear (N = 1702, M = .45), sadness (N = 1494, M = .40), trust (N = 1470, M = .44), and anticipation (N = 1376, M = .37). The prevalence of complex/dyadic emotions and their interpretation is further discussed. In this contribution, we identified and discussed prevalent emotions such as fear, sadness, optimism, hope, despair, and outrage that mirror the psychological state of users and affect the medical choices they make. The insights gained in our study contribute to the understanding of the barriers and reinforcers to participation in trials and can improve the ability of healthcare professionals to assist patients when confronted with this choice.

Emotional Dynamics in Fibromyalgia: Pain, Fatigue, and Stress Moderate Momentary Associations Between Positive and Negative Emotions

Affective disruptions, particularly deficits in positive affect, are characteristic of fibromyalgia (FM). The Dynamic Model of Affect provides some explanations of affective disruptions in FM, suggesting that the inverse association between positive and negative emotions is stronger when individuals with FM are under greater stress than usual. However, our understanding of the types of stressors and negative emotions that contribute to these affective dynamics is limited. Using ecological momentary assessment (EMA) methods, 50 adults who met the FM survey diagnostic criteria rated their momentary pain, stress, fatigue, negative emotions (depression, anger, and anxiety), and positive emotions 5X/day for eight days using a smartphone application. Results of multilevel modeling indicate that, consistent with the Dynamic Model of Affect, there was a stronger inverse association between positive emotion and negative emotions during times of greater pain, stress, and fatigue. Importantly, this pattern was specific to depression and anger, and was not present for anxiety. These findings suggest that fluctuations in fatigue and stress may be just as important or more important than fluctuations in pain when understanding the emotional dynamics in FM. In addition, having a more nuanced understanding of the role that different negative emotions play may be similarly important to understanding emotional dynamics in FM. PerspectiveThis article presents new findings on the emotional dynamics in FM during times of increased pain, fatigue, and stress. Findings highlight the need for clinicians to conduct a comprehensive evaluation of fatigue, stress, and anger in addition to more routinely assessed depression and pain when working with individuals with FM.

For Deliberation Sake, Show Some Constructive Emotion! How Different Types of Emotions Affect the Deliberative Quality of Interactive User Comments

Deliberation is classically understood as a communication process where equal participants justify their positions in a respectful, reciprocal, argumentative manner. However, critical scholars have argued for a concept of deliberation that incorporates other forms of communication beyond argumentation, for example, expressions of emotions. While previous research focused on differences between positive and negative emotions, we introduce a distinction between constructive and non-constructive expressions of emotions. Whilst constructive emotions focus on the discussed issue, non-constructive emotions refer to other participants. We draw on a quantitative relational content analysis of user comments written in an online-participation platform. The results show a positive effect of constructive expressions of emotions on the deliberative quality of interactive user comments and a negative effect of non-constructive expressions of emotions. Overall, we conclude that emotions can promote the deliberative quality of interactive user comments if they are not focused on other participants but on the discussed issue.

Feature Elimination through Data Complexity for Error-Correcting Output Codes based micro-expression recognition

Micro-Expression (ME) is a kind of short-lived and uncontrollable facial expressions. The MEs recognition task poses a great challenge to both the psychological and computer vision research communities. In this study, a Feature Elimination through Data Complexity-based Error-Correcting Output Codes (FEDC-ECOC) algorithm is proposed. In the generation of the coding matrix, a set of data complexity measures are utilized as the division criteria to form a coding matrix. Meanwhile, the sliding window and the greedy search algorithm are applied to improve the discriminative ability of the coding matrix for various emotion types. On the other hand, this study proposes a feature selection algorithm to identify essential features to enhance the performance of classifiers. Comprehensive experiments are conducted, and the results confirm the robustness and effectiveness of our FEDC-ECOC. Detailed analysis is given to further provide insights of the proposed method.

Emotion recognition links to reactive and proactive aggression across childhood: A multi-study design.

Difficulty recognizing negative emotions is linked to aggression in children. However, it remains unclear how certain types of emotion recognition (insensitivities vs. biases) are associated with functions of aggression and whether these relations change across childhood. We addressed these gaps in two diverse community samples (study 1: aged 4 and 8; N = 300; study 2: aged 5 to 13, N = 374). Across studies, children performed a behavioral task to assess emotion recognition (sad, fear, angry, and happy facial expressions) while caregivers reported children's overt proactive and reactive aggression. Difficulty recognizing fear (especially in early childhood) and sadness was associated with greater proactive aggression. Insensitivity to anger - perceiving angry faces as showing no emotion - was associated with increased proactive aggression, especially in middle-to-late childhood. Additionally, greater happiness bias - mistaking negative emotions as being happy - was consistently related to higher reactive aggression only in early childhood. Together, difficulty recognizing negative emotions was related to proactive aggression, however, the strength of these relations varied based on the type of emotion and developmental period assessed. Alternately, difficulty determining emotion valence was related to reactive aggression in early childhood. These findings demonstrate that distinct forms of emotion recognition are important for understanding functions of aggression across development.

Antecedents and outcomes of Generation Z consumers’ contrastive and assimilative upward comparisons with social media influencers

PurposeThis study aims to focus on the dynamics in influencer-consumer relationships to understand how Generation Z consumers’ identification and social comparison with influencers shape their response to influencers’ branded posts. Specifically, this study investigates how perceived similarity and wishful identification lead to distinct social comparison mechanisms that affect Generation Z consumers’ self-improvement motives, which, in turn, drive their message engagement, brand attitudes and purchase intentions.Design/methodology/approachAn online survey was conducted with 295 college students who are digital natives and whose purchase decisions are heavily influenced by social media influencers.FindingsThe study findings confirmed that perceived similarity positively influenced assimilative comparison emotions of optimism, admiration and aspiration while negatively influenced contrastive comparison emotions of envy, depression and resentment. Wishful identification positively affected both assimilative and contrastive comparison emotions. Both types of social comparison emotions further affected consumers’ motivations to follow the influencer for self-improvement, thereby enhancing their brand attitude, purchase intention and engagement behaviors.Originality/valueThis study is one of the earliest attempts to investigate the relationship dynamics between influencers and consumers from the lens of social comparison. The study examines the antecedents of perceived similarity and wishful identification, the mediators of upward comparison emotions and self-improvement motives and the brand evaluation outcomes of message engagement, brand attitude and purchase intention.

Keputusan Pembelian Emosional Pelanggan Beretnis Bugis di Kota Makassar, Indonesia

The Bugis are known as accomplished sailors, who really consider any conditions they will face before carrying out their activities. However, if it is related to consumption patterns, there has been a shift in values, where the visible phenomenon is that this shopping center is never empty even on weekdays or on religious holidays, it is always full of visitors. This behavior can illustrate that currently Bugis consumers make shopping centers a place of activity and Bugis consumer behavior is included in the emotional (hedonic) type, but has not yet answered this phenomenon. Thus, this study aims to explore whether the buying behavior of the Bugis community includes emotional buying behavior when shopping at the mall. This study uses a qualitative approach, which aims to explore the true meaning of the reasons behind the buying behavior of Bugis customers. Research informants obtained by purposive sampling technique. The results of the study show that the motive behind buying behavior at the mall in Makassar city is the existence of an emotional buying motive in shopping caused by (1). The mall is a suitable place for the customer's social status (2) the desire to find a relaxed and comfortable atmosphere, (2). desire for refreshment and recreation with family, (3). Want to spend time at the mall while paying attention to the latest clothing trends worn by other visitors, and (5) desire to socialize while doing business or looking for a side job.

Research on emotion classification technology of movie reviews based on topic attention mechanism and dual channel long short term memory.

In recent years, with the popularity of the Internet, more and more people like to comment on movies they have watched on the film platform after watching them. These reviews hide the reviewers' feedback on films. Mining the emotional orientation information in these reviews can provide consumers with shopping references and help businesses optimize film works and improve business strategies. Therefore, the emotional classification of film reviews has high research value because few emotion dictionaries and analysis tools are available for reference and use in film reviews. The accuracy of emotion classification still needs to be improved. This study introduces the attention mechanism and dual channel long short term memory (DC-LSTM) while building the emotion dictionary in the field of Chinese film review. It classifies Chinese film reviews in terms of topic-based fine-grained emotion. First, the emotion vector is constructed using the constructed movie review emotion lexicon. The semantic vector obtained by the Word2vector tool is input to LSTM to encode the comment text. Then, the topic attention module is used to decode. Finally, the final emotion classification result is obtained through the softmax function of the entire link layer and the output layer. The thematic attention modules constructed in this study are independent of each other for attention parameter adjustment and learning. One attention module corresponds to one film theme. In this study, eight themes, including "plot," "special effects," "original work," "music," "thought," "theme," "acting skills," and "joke," were extracted, and each theme was classified into three types of emotions: "positive," "neutral," and "negative." The experimental results on the crawled Chinese film review dataset show that the proposed algorithm is superior to some existing algorithms and models in accuracy, precision, recall and F1 measure. The DCLSTM based on the thematic attention mechanism (DCLSTM-TAM) model constructed in this study introduces the emotion vector into the network and adds the theme attention mechanism. It can not only classify the emotion for different topics of a film review but also effectively deal with film reviews with fuzzy emotional tendencies. It realizes the fine-grained emotion classification of film topics and improves the accuracy of emotion classification of film reviews. The emotion classification method and model proposed in this study have good transferability, and the change of training corpus is also applicable to other short text fields.