Quantification of vascular function changes under different emotion states: A pilot study

Recent studies have indicated that physiological parameters change with different emotion states. This study aimed to quantify the changes of vascular function at different emotion and sub-emotion states. Twenty young subjects were studied with their finger photoplethysmographic (PPG) pulses recorded at three distinct emotion states: natural (1 minute), happiness and sadness (10 minutes for each). Within the period of happiness and sadness emotion states, two sub-emotion states (calmness and outburst) were identified with the synchronously recorded videos. Reflection index (RI) and stiffness index (SI), two widely used indices of vascular function, were derived from the PPG pulses to quantify their differences between three emotion states, as well as between two sub-emotion states. The results showed that, when compared with the natural emotion, RI and SI decreased in both happiness and sadness emotions. The decreases in RI were significant for both happiness and sadness emotions (both P< 0.01), but the decreases in SI was only significant for sadness emotion (P< 0.01). Moreover, for comparing happiness and sadness emotions, there was significant difference in RI (P< 0.01), but not in SI (P= 0.9). In addition, significant larger RI values were observed with the outburst sub-emotion in comparison with the calmness one for both happiness and sadness emotions (both P< 0.01) whereas significant larger SI values were observed with the outburst sub-emotion only in sadness emotion (P< 0.05). Moreover, gender factor hardly influence the RI and SI results for all three emotion measurements. This pilot study confirmed that vascular function changes with diffenrt emotion states could be quantified by the simple PPG measurement

The Dark Side of E-sports: The Role of Player Emotions and Cyberbullying in MOBA

Within the context of Multiplayer Online Battle Arena video games (MOBAs), Cyberbullying Behavior (CB) has become a complex and yet unsolved socio-technological challenge. While significant work has been done recently, there is a lack of studies in relation to the role of players’ emotions and CB as well as a lack of theory-guided approaches for curbing CB in MOBAs. In this work, we developed a holistic framework for understanding the relationship between player emotions (achievement, challenge, and loss) and their CB in different phases (early game, mid game, and late game) of MOBAs. For this, we used a qualitative approach comprising 1,048,575 chat logs and interviews with 21 MOBA players. The preliminary results indicate that different emotions arise at different phases of MOBA games that gives rise to cyberbullying behavior in the players

Advanced EEG Signal Based Min to Mean Algorithm Approach For Human Emotion Taxonomy And Mental State Analysis

With electroencephalography (EEG) brain waves alone, it is full-scale phenomena in the field of computer-brain interface DNN, CNN, and SVM have improved detection and prediction accuracy in a number of researches during the last several years. But when it comes to recognizing global reliance, both deep learning and SVM have obvious limits. Pre-processing, extraction capabilities, and network design are the most common techniques used in deep learning models today, yet they are still unable to produce reliable results in noisy and sparse datasets. Any dataset, no matter how little or large, may suffer from poor SVM performance due to overlapping target instructions and boundaries. There are many different sorts of emotions that may be classified using the particular approach employed in this research. In order to get a whole picture of a person's mental state, it is best to use a "Min of mean” proposed technique. After comparison to the referential mean, a feeling is divided into one of four emotional quadrants. The MIN Max range is used to further split the emotion into 12 subcategories based on the amount of arousal. The proposed set of rules performed better than existing methods. Research on multi-class emotion reputation has shown that, compared to more recent studies, the proposed technique may be rather strong. It is possible to analyze a person's mental health by using the emotional spectrum, which has an accuracy rate of above 90%

An Investigation of Concepts and Words Used By Turkish Children and Teenagers to Define Their Perceptions Regarding Events Involving Emotional Situations

The present study aimed to determine the concepts and words children and teenagers use to define their perceptions of events that involve emotions. It attempted to identify words that children and teenagers use to express emotions and the differences between children and teenagers in this regard. The study focused on 214 children and adolescents, including 124 students from four primary schools in Eskisehir, Turkey (59 girls and 65 boys) and 90 students from one secondary school and two high schools (39 girls and 51 boys). The students were given a questionnaire that asked 10 questions focusing on three emotional categories (happiness, sadness and fear), two types of self-descriptive adjectives (positive and negative) and two neutral categories (semantically related and semantically unrelated). The questionnaire was developed from a form used by Doost et al. (1999). We concluded that the positive and negative expressions used by both children and teenagers were examined regardless of frequency of use, and the constituents, which were basically detected, were gathered under definite titles

Computational emotion classification for genre corpora of German tragedies and comedies from 17th to early 19th century

This article presents a method of emotion analysis for German drama from the 17th to the 19th century that significantly goes beyond previous research approaches in computational literary studies. It is based on annotations of 17 dramatic texts resulting in 11,939 annotations which were used as training material to fine-tune a German language BERT model that achieves an average accuracy of 73% for the single-label emotion classification of fourteen emotion types in cross-validation. We apply the emotion classification on a corpus of 141 comedies and 92 tragedies to compare these genres. For tragedies, the mean proportion percentages of ‘suffering’ and ‘abhorrence’ are higher than for comedies. Inversely, mean percentages of ‘anger’ and ‘joy’ are higher for comedies than for tragedies. A new finding is the surprisingly high proportion of ‘anger’ in comedies. Emotion distribution of the last scenes in dramatic texts also proves the quality of the classified data in terms of literary studies. In addition, the emotion distribution for several subgenres of comedy is investigated including non-canonical works of wide circulation which reached the recipients directly through the depicted emotions in the Kasperl Plays. Comedies from 1740 to 1770 are characterized by a pairing of higher amounts of ‘friendship’ and ‘love’. Satirical comedies from the same period stand out due to high rates of ‘anger’ as well as ‘suffering’. The very successful Kasperl plays turn out to be characterized by a comparatively large percentage of ‘schadenfreude’ and ‘joy’

Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary Language

We present results of a project on emotion classification on historical German plays of Enlightenment, Storm and Stress, and German Classicism. We have developed a hierarchical annotation scheme consisting of 13 sub-emotions like suffering, love and joy that sum up to 6 main and 2 polarity classes (positive/negative). We have conducted textual annotations on 11 German plays and have acquired over 13,000 emotion annotations by two annotators per play. We have evaluated multiple traditional machine learning approaches as well as transformer-based models pretrained on historical and contemporary language for a single-label text sequence emotion classification for the different emotion categories. The evaluation is carried out on three different instances of the corpus: (1) taking all annotations, (2) filtering overlapping annotations by annotators, (3) applying a heuristic for speech-based analysis. Best results are achieved on the filtered corpus with the best models being large transformer-based models pretrained on contemporary German language. For the polarity classification accuracies of up to 90% are achieved. The accuracies become lower for settings with a higher number of classes, achieving 66% for 13 sub-emotions. Further pretraining of a historical model with a corpus of dramatic texts led to no improvements

Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMs

Understanding and identifying the causes behind developers' emotions (e.g., Frustration caused by `delays in merging pull requests') can be crucial towards finding solutions to problems and fostering collaboration in open-source communities. Effectively identifying such information in the high volume of communications across the different project channels, such as chats, emails, and issue comments, requires automated recognition of emotions and their causes. To enable this automation, large-scale software engineering-specific datasets that can be used to train accurate machine learning models are required. However, such datasets are expensive to create with the variety and informal nature of software projects' communication channels. In this paper, we explore zero-shot LLMs that are pre-trained on massive datasets but without being fine-tuned specifically for the task of detecting emotion causes in software engineering: ChatGPT, GPT-4, and flan-alpaca. Our evaluation indicates that these recently available models can identify emotion categories when given detailed emotions, although they perform worse than the top-rated models. For emotion cause identification, our results indicate that zero-shot LLMs are effective at recognizing the correct emotion cause with a BLEU-2 score of 0.598. To highlight the potential use of these techniques, we conduct a case study of the causes of Frustration in the last year of development of a popular open-source project, revealing several interesting insights

Dynamics of the "Divestment-Investment" Process: An Investigation of the Process of Adaptation Occurring in the Ecological Transition of Leaving Home to Live at College

The overall focus of this study was to investigate the initial adaptation process to college. The adaptation process was viewed as embedded within the ecological transition of leaving home to live at college. The primary psychosocial tasks which provided the process criterion for adaptation were contained in the "divestment-investment" process. The study was divided into 2 phases which used a cross-sectional design. The major hypotheses of Phase I predicted a relationship between perceived adjustment and perceived family characteristics, as well as between perceived adjustment and separation feelings. For Phase II the hypotheses predicted a relationship between perceived adjustment and four adaptation criterion variables: information processing, autonomy, motivation, self-esteem. The Ss for Phase I of the study consisted of 105 male freshmen from Columbia College and the School of Engineering. The Ss were administered the Bell Adjustment Inventory which determined perceived adjustment; the Family Adaptation and Cohesion Evaluation Scale which enabled assessment of perceived family cohesion and perceived family adaptation; a Separation scale which yielded scores on separation affect; and a demographic questionnaire which measured environmental variables. In Phase II, adjustment scores derived from Phase I were compared with the four adaptation criterion variables. The four criterion variables for adaptation were determined by four judges using a content analysis of taped interviews with Ss from a subgroup (N = 30) of the sample population. A multivariate analysis of variance, Eta and Oneway analyses were used to obtain results from the data of Phase I. A univariate analysis of variance, and Eta analysis were used to analyze Phase II data. The major findings of Phase I were that relationships were found to exist between the Perceived Family Cohesion scores and the perceived adjustment scores, as well as between Perceived Family Cohesion scores and the Separation scores. The major findings of Phase II were that relationships were found to exist between the adaptation criterion variables of Motivation, Autonomy, and Self-esteem on the 3 Bell Adjustment groups for the two content areas of separation issues, and academic issues. On the basis of the information which this exploratory study has provided, it seems reasonable to suggest that the individual's perceived level of family cohesion was instrumental in his initial adaptation to college

Environmental Harshness and its Effect on Appetite and the Desire for Conspicuous Signalling Products

There is often an assumption that there is a right and a wrong way for consumers to behave. For example, with regard to eating, people should make food choices based on maximizing vitamins and minerals and not consuming more calories than one expends in a day. Likewise, it is assumed that buying products to conspicuously signal a message to another is wasteful and maladaptive. The research in this thesis challenges these assumptions and argues that these behaviours can be both adaptive and maladaptive depending on one’s environmental conditions. In this thesis, I describe three experiments that examine how perception of environmental harshness affects appetite for different types of foods. The data shows that food desirability in adulthood varies depending on early childhood socio-economic status, the type of environmental stressor (harsh social, harsh economic and harsh physical safety) and the intensity of the stressors within each of these environments. It was also found that different types of environmental harshness differentially affects food desire based on energy density and food category type. In addition to the experiments on harshness and food desirability, I have examined how environmental harshness affects desire for products that are used to conspicuously signal information to others. For example, under conditions of environmental stress, products may be used to advertise that a male possesses financial or physical power which is desirable to a potential mate. Likewise, a women may buy products to display she possess financial power or she may purchase products that augment her beauty and sexual attractiveness. These studies reveal that product desire is also affected by different types of environmental harshness and the intensity of the stress generated by these environmental conditions. Through the research described in this thesis, we gain a more comprehensive understanding of the proximate variables that influence two subsets of consumer behaviour, namely food desire and product signalling, and how these behaviours may have been selected for due to their adaptive value