Article

Emotion Knowledge: Further Exploration of a Prototype Approach

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Abstract

Recent work on natural categories suggests a framework for conceptualizing people's knowledge about emotions. Categories of natural objects or events, including emotions, are formed as a result of repeated experiences and become organized around prototypes (Rosch, 1978); the interrelated set of emotion categories becomes organized within an abstract-to-concrete hierarchy. At the basic level of the emotion hierarchy one finds the handful of concepts (love, joy, anger, sadness, fear, and perhaps, surprise) most useful for making everyday distinctions among emotions, and these overlap substantially with the examples mentioned most readily when people are asked to name emotions (Fehr & Russell, 1984), with the emotions children learn to name first (Bretherton & Beeghly, 1982), and with what theorists have called basic or primary emotions. This article reports two studies, one exploring the hierarchical organization of emotion concepts and one specifying the prototypes, or scripts, of five basic emotions, and it shows how the prototype approach might be used in the future to investigate the processing of information about emotional events, cross-cultural differences in emotion concepts, and the development of emotion knowledge.

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... Podoynitsyna, Van der Bij, & Song, 2012;Hassett et al., 2018) was introduced by Laros & Steenkamp (2005) who categorised emotions as four positive (contentment, happiness, love and pride) and four negative basic emotions (anger, fear, sadness and shame) based on a systematic review of emotions studies from the 80s. This research, however, adopts the comprehensive classification of Shaver et al. (1987Shaver et al. ( , pp. 1068) who identified six basic emotions (Love, Joy, Surprise, Anger, Sadness, Fear) and one hundred and thirty-four subcategories. ...
... While calculating the frequencies of the emerged explicit emotions was inductive, linking these emotions to their sub-emotions was deductive. Returning to the psychology literature, the researcher managed to cite some subcategories of emotions that she surprisingly discovered them to be linked to anger based on Shaver's (1987) categorisation of emotions. ...
... The process of capturing implicit emotions was also abductive. To be able to link anger with retention and termination decisions, the researcher returned to theory, basically Roseman (2011) andShaver (1987), to link the behavioural and emotional consequences of anger to these two decisions and the triggers of anger to the context. Utilising these behavioural, motivational and subjective cues and characteristics of emotions from the psychology literature was deductive but connecting these captured emotions to their triggering events, sources and the final decisions of retention and termination was inductive. ...
Thesis
This research focuses on anger and sunk cost effects as sources of cognitive bias and also portfolio interactions in relation to the retention/termination decisions on projects. Departing from a traditionally narrow and quantitative perspective of traditional project appraisal, this study investigates a wider psychological view of investment project decisions within four project management groups. The thesis emphasises that the role of the specific emotion of anger is influenced by the past sunk cost of projects and the effects of a portfolio of projects across the whole firm. In the sense that project retention is perceived to be a positive outcome of anger, it has arguably been neglected in empirical entrepreneurship and strategic decision-making research, but this study claims that the retention and termination of projects may be analysed using psychological theories of emotions. A case study based on a Palestinian holding company, therefore, investigates the influence of anger, the sunk cost effect and portfolio considerations on project retention and termination. The holding company under study operates in an uncertain political context likely to be a rich laboratory eliciting high levels of anger, thus highlighting their role. This study conducts fifteen emotion assessment surveys using a STAXI-2 inventory and content and thematic analyses of fifteen interviews, adopting multi-levels of analysis, and claims to make contributions to the entrepreneurship, strategic decision-making and psychology literatures. The analysis reports that anger has an important emotional influence on decisions. It demonstrates three main findings, i.e. mostly positive associations between anger, the sunk cost effect and portfolio considerations and project retention. It also presents four subsidiary findings. Hope emerged as the second most important emotion and is claimed to be associated with project retention. Other emotions also co-exist with anger and may have influenced retention decisions, and findings reveal an association between corporate identity (i.e. a factor emerged from data) and project retention. Finally, in an atypical case, anger is found to encourage project termination.
... Active and passive BH correspond to what Kucuk (2018, p. 4) defines as "behavioral" and "attitudinal" BH, respectively. Finally, Zhang and Laroche (2020) went a step ahead to examine BH emotions beyond anger-like (CAD, disappointment, shame and dehumanization) emotions to include sadness-and fear-related emotions drawing upon Shaver et al. (1987), besides Sternberg's(2003) duplex theory. ...
... Moreover, we see that except for a few theories (Sternberg's duplex theory of hate, Attachment-Aversion model, Shaver et al., 1987), all the theories listed in Table 1 have been used to elucidate either the cause or the effect relationships of BH, rather than exploring the BH construct per se. While Sternberg's (2003) duplex theory and Shaver et al. (1987) have been used to define hate from an emotional perspective, AAR model views hate from a relational standpoint. ...
... Moreover, we see that except for a few theories (Sternberg's duplex theory of hate, Attachment-Aversion model, Shaver et al., 1987), all the theories listed in Table 1 have been used to elucidate either the cause or the effect relationships of BH, rather than exploring the BH construct per se. While Sternberg's (2003) duplex theory and Shaver et al. (1987) have been used to define hate from an emotional perspective, AAR model views hate from a relational standpoint. These further shed light on the divergent conceptualizations of BH, identified earlier in Section 2, and call for a more comprehensive theoretical framework to study BH. ...
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Purpose A conceptual confusion has evolved in brand hate (BH) research mainly due to multiple conceptualizations, models and constructs in the field. As such, there is an urgent need to bring these insights together for a holistic understanding of research in BH, fostering its growth. This paper aims to fill this theoretical gap by bringing together the field of BH and delineating opportunities for further research. Design/methodology/approach A systematic literature review was conducted for a period of about two decades, from 1998 to August 2021. The authors included the English articles published in peer-reviewed academic journals with full texts relevant to this study, leading to a usable sample of 55 articles. Findings The authors’ findings reveal that the literature has inadequately distinguished BH as emotion and relationship, while the theoretical domain used to explore BH remains largely dominated by the psychology literature. Furthermore, BH research has primarily focused on services, with little distinction made between hatred across product and service context, with most studies set in developed countries. The authors further identify the need to investigate boundary conditions influencing BH and develop a more robust measure of BH to capture its dynamic facet. Research limitations/implications By presenting a comprehensive and up-to-date overview of the research in BH and highlighting the future research avenues, this study is believed to spur scholarly research and serve as a valuable tool for the researchers in advancing the research in BH. Practical implications Analysis of determinants and antecedents of BH provide managers an opportunity to nip the evil in the bud by preventing such situations that may lead to BH. Furthermore, insights into different BH consequences and boundary conditions allow brand managers to devise appropriate strategies to mitigate adverse reactions and foster positive consumer–brand relationships. Originality/value This study provides a thorough analysis of the current state of BH research in one place and draws a road map for scholars to further the research in this area.
... Podoynitsyna, Van der Bij, & Song, 2012;Hassett et al., 2018) was introduced by Laros & Steenkamp (2005) who categorised emotions as four positive (contentment, happiness, love and pride) and four negative basic emotions (anger, fear, sadness and shame) based on a systematic review of emotions studies from the 80s. This research, however, adopts the comprehensive classification of Shaver et al. (1987Shaver et al. ( , pp. 1068) who identified six basic emotions (Love, Joy, Surprise, Anger, Sadness, Fear) and one hundred and thirty-four subcategories. ...
... While calculating the frequencies of the emerged explicit emotions was inductive, linking these emotions to their sub-emotions was deductive. Returning to the psychology literature, the researcher managed to cite some subcategories of emotions that she surprisingly discovered them to be linked to anger based on Shaver's (1987) categorisation of emotions. ...
... The process of capturing implicit emotions was also abductive. To be able to link anger with retention and termination decisions, the researcher returned to theory, basically Roseman (2011) andShaver (1987), to link the behavioural and emotional consequences of anger to these two decisions and the triggers of anger to the context. Utilising these behavioural, motivational and subjective cues and characteristics of emotions from the psychology literature was deductive but connecting these captured emotions to their triggering events, sources and the final decisions of retention and termination was inductive. ...
... To answer this RQ, we first create a new dataset for emotion classification based on GitHub issue and pull request discussions. We annotate the dataset based on the six emotion categories first introduced by Shaver [73], while also going beyond this categorization to a finer grained division using secondary and tertiary emotions. Using our dataset, we evaluate three of the most commonly used tools for software engineering emotion classification (ESEM-E [51], EMTk [9], SEntiMoji [13]), showing that their accuracy is further reduced compared to the original datasets that the tools were built and evaluated for. ...
... For instance, Ekman et al. [22] categorized emotions into six basic categories: Anger, Disgust, Fear, Joy, Sadness, and Surprise. On the other hand, Shaver et al. [73] identified six basic emotion categories: Love, Joy, Anger, Sadness, Surprise, and Fear. Shaver et al. expanded the basic set of emotions to secondary and tertiary levels in a tree-like structure. ...
... Novielli et al. [55] annotated a gold set from 4800 StackOverflow questions, answers, and comments. They labeled the sentences with Shaver et al [73]'s six basic categories. Venigalla et al. [82] analyzed 10996 commit messages related to software documentation update from 998 GitHub projected and mapped them into Plutchik's eight emotion categories [63]. ...
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Emotions (e.g., Joy, Anger) are prevalent in daily software engineering (SE) activities, and are known to be significant indicators of work productivity (e.g., bug fixing efficiency). Recent studies have shown that directly applying general purpose emotion classification tools to SE corpora is not effective. Even within the SE domain, tool performance degrades significantly when trained on one communication channel and evaluated on another (e.g, StackOverflow vs. GitHub comments). Retraining a tool with channel-specific data takes significant effort since manually annotating large datasets of ground truth data is expensive. In this paper, we address this data scarcity problem by automatically creating new training data using a data augmentation technique. Based on an analysis of the types of errors made by popular SE-specific emotion recognition tools, we specifically target our data augmentation strategy in order to improve the performance of emotion recognition. Our results show an average improvement of 9.3% in micro F1-Score for three existing emotion classification tools (ESEM-E, EMTk, SEntiMoji) when trained with our best augmentation strategy.
... Subsequent research, using a variety of different methods, repeatedly yielded these or similar dimensions when analyzing subjective feelings in different cultures (e.g. Fontaine et al., 2007;Morgan & Heise, 1988;Russell & Mehrabian, 1977;Scherer, 2005;Shaver et al., 1987;Traxel & Heide, 1961). An example is presented in Figure 1, where the first two dimensions, named valence (positive-negative) and control (high-low), are represented on the horizontal and vertical axes, whereas the third dimension, intensity, is indicated by the size of the circles. ...
... Especially for displeasing, activated emotions, it is important that a third dimension of dominance-submission is introduced in order to distinguish such contrasting feelings as anger signaling potency, and fear signaling a weak position; a problem of the two-dimensional solution that was mentioned already in the review by Larsen and Diener (1992). Shaver et al. (1987) got a corresponding three-dimensional result on the basis of a prototype approach, whereby anger, fear and sorrow as unpleasant feelings differed only in potency. Moreover, anger and fear could be clearly distinguished in a real-life induction on physiological measures (Stemmler, 1989). ...
... Each rating yielded a three-dimensional structure with similar locations for feelings and words (Traxel & Heide, 1961). The relationship of feelings and semantics could also be established in the emotion-word studies mentioned above, which found that the same three dimensions as with the semantic differential are necessary to distinguish emotions adequately but that these are not evenly spread over the semantic space (Morgan & Heise, 1988;Shaver et al., 1987). One can reasonably conclude that this result is not simply due to the affective three-dimensionality of language. ...
Article
A review of dimensional research about (the perception of) feelings, non-verbal and verbal communication, behavior and personality reveals in each domain three very similar dimensions. They originated from diverse research areas, often received different names and are conceptually not identical. Yet, the first dimension seems to share in all five areas a general positive versus negative evaluation (e.g. happiness–disgust or friendliness–hostility), the second a strong versus weak characterization (e.g. anger–fear or dominance–submission) and the third dimension an active versus passive impression (e.g. ecstasy–boredom or high–low arousability). These three dimensions are likely to function as fundamental dimensions of interaction and communication as perceived and enacted by humans of all (investigated) cultures. They are interpreted as a universal socio-emotional space that corresponds to an evolutionary need for coordination between individuals. They are implied in the logic of game, exchange or interdependence theory, and manifest themselves in the cultural meanings predicted by affect control theory. The presented overview and reconstruction combines the largely fragmented views of several diverse research domains into a perspective that fosters interdisciplinary understanding and integrative theory-building about human sociality within and between the social sciences with extensions into the natural sciences and humanities.
... Shaver et al. [17] asked psychology students to perform a hierarchical sorting of emotions. This resulted in three levels in the emotion hierarchy. ...
... In the second level, five basic emotion categories were used: love, joy, anger, sadness, fear. The sixth emotion surprise, according to Shaver et al. [17], was not considered a basic category because it rarely occurred in the studies, but was nevertheless considered for further analysis. In the third level, emotions are divided into subcategories (for example, sadness is divided into, e.g., guilt or agony). ...
... In the third level, emotions are divided into subcategories (for example, sadness is divided into, e.g., guilt or agony). Parrott [18] expanded Shaver et al.'s framework [17] with further details and also distinguishes between three levels: primary emotion, secondary emotion, and tertiary emotion. With the addition of surprise, the primary emotions are equal to the second level of Shaver et al.'s model [17]. ...
Preprint
Social aspects of software projects become increasingly important for research and practice. Different approaches analyze the sentiment of a development team, ranging from simply asking the team to so-called sentiment analysis on text-based communication. These sentiment analysis tools are trained using pre-labeled data sets from different sources, including GitHub and Stack Overflow. In this paper, we investigate if the labels of the statements in the data sets coincide with the perception of potential members of a software project team. Based on an international survey, we compare the median perception of 94 participants with the pre-labeled data sets as well as every single participant's agreement with the predefined labels. Our results point to three remarkable findings: (1) Although the median values coincide with the predefined labels of the data sets in 62.5% of the cases, we observe a huge difference between the single participant's ratings and the labels; (2) there is not a single participant who totally agrees with the predefined labels; and (3) the data set whose labels are based on guidelines performs better than the ad hoc labeled data set.
... Researchers have studied the lexicon of emotional words and how emotional experiences are organized or structured in our minds in different human languages. Studies on English mental lexicons of emotions have revealed the hierarchical structure of different emotional words using a prototype approach 16 . With the same hierarchical approach, other languages such as Basque and Indonesian were explored as well 17,18 . ...
... Hierarchical clustering is widely used in examining structures and dimensions of emotion lexicons in a variety of languages [16][17][18]27 . To obtain comparable clusters, we adopted a similar approach. ...
Article
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Emotions are not necessarily universal across different languages and cultures. Mental lexicons of emotions depend strongly on contextual factors, such as language and culture. The Chinese language has unique linguistic properties that are different from other languages. As a main variant of Chinese, Cantonese has some emotional expressions that are only used by Cantonese speakers. Previous work on Chinese emotional vocabularies focused primarily on Mandarin. However, little is known about Cantonese emotion vocabularies. This is important since both language variants might have distinct emotional expressions, despite sharing the same writing system. To explore the structure and organization of Cantonese-label emotion words, we selected 79 highly representative emotion cue words from an ongoing large-scale Cantonese word association study (SWOW-HK). We aimed to identify the categories of these emotion words and non-emotion words that related to emotion concepts. Hierarchical cluster analysis was used to generate word clusters and investigate the underlying emotion dimensions. As the cluster quality was low in hierarchical clustering, we further constructed an emotion graph using a network approach to explore how emotions are organized in the Cantonese mental lexicon. With the support of emotion knowledge, the emotion graph defined more distinct emotion categories. The identified network communities covered basic emotions such as love, happiness, and sadness. Our results demonstrate that mental lexicon graphs constructed from free associations of Cantonese emotion-label words can reveal fine categories of emotions and their relevant concepts.
... disgust). Of the six primary emotions [65], threeanger, fear, and sadnesstranslate into negative sentiment. The other threejoy, love, and surprisecontribute to positive sentiment. ...
... The other threejoy, love, and surprisecontribute to positive sentiment. Each of the six primary emotions is made up of various secondary and tertiary emotions [65]. Polarity classification, which assesses whether a message is positive, negative or neutral [66], is a cornerstone of sentiment analysis. ...
Article
Social acceptance is essential to effective sustainable energy policy implementation. Social media offer new platforms to support policy work and, by allowing emotional expressions, help to create an emotion discourse. Emotions and the discourse around them impact social acceptance by influencing organizational legitimacy, supporting and disrupting institutions, and energizing policy actors. This research investigates how social media analytics (SMA) can be used to decode the emotion discourse on sustainable energy to fulfill diverse informational goals of policy actors. Applying SMA to 6528 Twitter messages for 27 U.S. electricity utilities over five months, we demonstrate how to measure and compare the emotion discourse of utilities over time. Using a variety of SMA techniques, we find the emotion discourse around sustainable energy varies across utilities in terms of both magnitude and polarity and we uncover four clusters of utilities having similar patterns of emotion discourse. We further identify three anomalous emotional events. SMA also reveal that joy and sadness are, respectively, the most common positive and negative emotions expressed. Finally, we use SMA to reveal how different actors contribute to the emotion discourse: utility followers are predominately responsible for negative affect in the emotion discourse. This work serves as a proof-of-concept showing how SMA can complement other techniques for gauging social acceptance, informing policy, managing sustainable energy programs, and developing effective communication strategies.
... Emotions by groups is a concept developed by P. Shaver et al. [21] and also featured by W.G. Parrot [22]. This refers to the fact that starting from six primary emotions (love, joy, surprise, anger, sadness, fear), one can define related secondary and tertiary emotions. ...
... The last step in our analysis consisted of evaluating the emotions algorithm. For this, we divided the emotions into three categories (positive, negative and neutral) by considering a series of emotion classification studies ( [19][20][21][22]), as follows: positive emotions-'loved', 'attracted', 'happy', 'lustful', 'fearless', 'ecstatic', 'esteemed', 'safe', 'adequate', 'focused', 'entitled', 'independent', 'free', 'attached'; negative emotions-'sad', 'fearful', 'angry', 'bored', 'embarrassed', 'powerless', 'surprise', 'hated', 'alone', 'anxious', 'cheated', 'singled out', 'belittled', 'lost', 'burdened', 'alone', 'demoralized', 'apathetic', 'obsessed', 'derailed', 'codependent'; neutral emotions-'average', 'free'. ...
Article
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Reading is not only a recreational activity; it also shapes the emotional and cognitive competences of the reader. In this paper, we present a method and tools for the analysis of emotions extracted from online reviews of literature books. We implement a scraper to create a new experimental dataset of reviews gathered from Goodreads, a website dedicated to readers that contains a large database of books and readers’ reviews. We propose a system which extracts the emotions from the reviews and associates them with the reviewed book. Afterwards, this information can be used to find similarities between the books based on readers’ impressions. Lastly, we show the experimental setup, consisting of the user interface developed for the proposed system, together with the experimental results.
... In the literature, researchers and psychologists have proposed a variety of emotion categorization models [12][13][14][15][16][17]35]. Shaver et al. [13] devised one of the earliest emotion modeling techniques. ...
... In the literature, researchers and psychologists have proposed a variety of emotion categorization models [12][13][14][15][16][17]35]. Shaver et al. [13] devised one of the earliest emotion modeling techniques. The authors categorized emotions into prototypes assuming that various portions of emotional information build up an organized whole. ...
Article
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Emotion identification based on multimodal data (e.g., audio, video, text, etc.) is one of the most demanding and important research fields, with various uses. In this context, this research work has conducted a rigorous exploration of model-level fusion to find out the optimal multimodal model for emotion recognition using audio and video modalities. More specifically, separate novel feature extractor networks for audio and video data are proposed. After that, an optimal multimodal emotion recognition model is created by fusing audio and video features at the model level. The performances of the proposed models are assessed based on two benchmark multimodal datasets namely Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Surrey Audio-Visual Expressed Emotion (SAVEE) using various performance metrics. The proposed models achieve high predictive accuracies of 99% and 86% on the SAVEE and RAVDESS datasets, respectively. The effectiveness of the models are also verified by comparing their performances with the existing emotion recognition models. Some case studies are also conducted to explore the model’s ability to capture the variability of emotional states of the speakers in publicly available real-world audio-visual media.
... This means that we do a document-level sentiment polarity assignment, where we assign a polarity label to the post based on the overall sentiment expressed in the post. For sentiment polarity assessment, we followed the emotion taxonomy of Shaver et al. (1987). The taxonomy is defined as a hierarchical tree of emotions that organizes emotion levels into clusters. ...
... Closely related to topics of productivity and well-being is the research on sentiment and emotions expressed by software developers. In our work we followed the emotion taxonomy of Shaver et al. (1987) that has been previously used by Calefato et al. (2018). Manual labeling of sentiment has been carried out in the past for such software engineering texts as Jira issues (Kaur et al. 2018), Gerrit code reviews (Ahmed et al. 2017), Stack Overflow posts , and source code comments expressing self-admitted technical debt (Fucci et al. 2021). ...
Article
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Many software developers started to work from home on a short notice during the early periods of COVID-19. A number of previous papers have studied the wellbeing and productivity of software developers during COVID-19. The studies mainly use surveys based on predefined questionnaires. In this paper, we investigate the problems and joys that software developers experienced during the early months of COVID-19 by analyzing their discussions in online forum devRant, where discussions can be open and not bound by predefined survey questionnaires. The devRant platform is designed for developers to share their joys and frustrations of life. We manually analyze 825 devRant posts between January and April 12, 2020 that developers created to discuss their situation during COVID-19. WHO declared COVID-19 as pandemic on March 11, 2020. As such, our data offers us insights in the early months of COVID-19. We manually label each post along two dimensions: the topics of the discussion and the expressed sentiment polarity (positive, negative, neutral). We observed 19 topics that we group into six categories: Workplace & Professional aspects, Personal & Family well-being, Technical Aspects, Lockdown preparedness, Financial concerns, and Societal and Educational concerns. Around 49% of the discussions are negative and 26% are positive. We find evidence of developers’ struggles with lack of documentation to work remotely and with their loneliness while working from home. We find stories of their job loss with little or no savings to fallback to. The analysis of developer discussions in the early months of a pandemic will help various stakeholders (e.g., software companies) make important decision early to alleviate developer problems if such a pandemic or similar emergency situation occurs in near future. Software engineering research can make further efforts to develop automated tools for remote work (e.g., automated documentation).
... Elevation "Elevation" is not a widely familiar archetypal emotion such as anger or happiness [67]. Haidt [68] defined elevation as a specific type of positive emotion (the opposite of social disgust) that is prompted by witnessing another person perform a virtuous act, especially one that benefits others. ...
Article
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Although gratitude is typically conceptualized as a positive emotion, it may also induce socially-oriented negative feelings, such as indebtedness and guilt. Given its mixed emotional experience, we argue that gratitude motivates people to improve themselves in important life domains. Two single-time point studies tested the immediate emotional and motivational effects of expressing gratitude. We recruited employees (N = 224) from French companies in Study 1 and students (N = 1,026) from U.S. high schools in Study 2. Participants in both studies were randomly assigned to either write gratitude letters to benefactors or outline their weekly activities (control condition). Expressing gratitude led to a mixed emotional experience (e.g., greater elevation and indebtedness) for both employees and students relative to controls. Students also felt more motivated and capable of improving themselves, as well as conveyed stronger intentions to muster effort towards self-improvement endeavors.
... Consequently, we realized that a single label for angered was too broad, and we constructed two types of angered: constructivelyangered and deconstructively-angered. Another example is excited and relaxed, which in other works such as [43] are classified under a single category joy. ...
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People come to social media to satisfy a variety of needs, such as being informed, entertained and inspired, or connected to their friends and community. Hence, to design a ranking function that gives useful and personalized post recommendations, it would be helpful to be able to predict the affective response a user may have to a post (e.g., entertained, informed, angered). This paper describes the challenges and solutions we developed to apply Affective Computing to social media recommendation systems. We address several types of challenges. First, we devise a taxonomy of affects that was small (for practical purposes) yet covers the important nuances needed for the application. Second, to collect training data for our models, we balance between signals that are already available to us (namely, different types of user engagement) and data we collected through a carefully crafted human annotation effort on 800k posts. We demonstrate that affective response information learned from this dataset improves a module in the recommendation system by more than 8%. Online experimentation also demonstrates statistically significant decreases in surfaced violating content and increases in surfaced content that users find valuable.
... The properties were based both on external natural language processing systems and platforms, and on internal algorithms that obtained the information through a text evaluation platform, which is completed by selected reviewers. The emotional vocabulary was organized by combining the hierarchy of emotions [40] and the tree of emotions [41]. Each emotional word was classified into 6 categories of primary emotions (love, joy, surprise, anger, sadness, and fear) and 25 subgroups of secondary emotions using affective and emotional text processing software (Indico, version 2020) that provides a toolkit of application programming interfaces (APIs). ...
Article
Background Social media is now a common context wherein people express their feelings in real time. These platforms are increasingly showing their potential to detect the mental health status of the population. Suicide prevention is a global health priority and efforts toward early detection are starting to develop, although there is a need for more robust research. Objective We aimed to explore the emotional content of Twitter posts in Spanish and their relationships with severity of the risk of suicide at the time of writing the tweet. Methods Tweets containing a specific lexicon relating to suicide were filtered through Twitter's public application programming interface. Expert psychologists were trained to independently evaluate these tweets. Each tweet was evaluated by 3 experts. Tweets were filtered by experts according to their relevance to the risk of suicide. In the tweets, the experts evaluated: (1) the severity of the general risk of suicide and the risk of suicide at the time of writing the tweet (2) the emotional valence and intensity of 5 basic emotions; (3) relevant personality traits; and (4) other relevant risk variables such as helplessness, desire to escape, perceived social support, and intensity of suicidal ideation. Correlation and multivariate analyses were performed. Results Of 2509 tweets, 8.61% (n=216) were considered to indicate suicidality by most experts. Severity of the risk of suicide at the time was correlated with sadness (ρ=0.266; P<.001), joy (ρ=–0.234; P=.001), general risk (ρ=0.908; P<.001), and intensity of suicidal ideation (ρ=0.766; P<.001). The severity of risk at the time of the tweet was significantly higher in people who expressed feelings of defeat and rejection (P=.003), a desire to escape (P<.001), a lack of social support (P=.03), helplessness (P=.001), and daily recurrent thoughts (P=.007). In the multivariate analysis, the intensity of suicide ideation was a predictor for the severity of suicidal risk at the time (β=0.311; P=.001), as well as being a predictor for fear (β=–0.009; P=.01) and emotional valence (β=0.007; P=.009). The model explained 75% of the variance. Conclusions These findings suggest that it is possible to identify emotional content and other risk factors in suicidal tweets with a Spanish sample. Emotional analysis and, in particular, the detection of emotional variations may be key for real-time suicide prevention through social media.
... Often, fear is accompanied by a perception of weakness and relative fatigue as well as a low potential for cooperation with the threatening event (Roseman 1984). Fear can be perceived as a social phenomenon, experienced by the individual within the cultural-social context; as it may be aroused by a threat to one's personal or social status, as well as one's identity and beliefs (Shaver et al. 1987). However, the most significant fear factor is the threat to one's physical existence (Jarymowicz and Bar-Tal 2006), such as in the case of COVID-19. ...
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The coronavirus pandemic has fundamentally shifted the way human beings interact, both as individuals and groups, in the face of such a widespread outbreak. This paper seeks to investigate the effects of COVID-19 on intergroup emotions and attitudes within an intractable intergroup conflict, specifically, through the lens of the Korean conflict. Using a two-wave, cross-sectional design, this study was able to track the profound psychological changes in intergroup emotions and attitudes both prior to the pandemic and during its onslaught. Results of these two wave representative samples show that South Korean citizens demonstrated higher levels of fear of their neighbors in North Korea after the outbreak of COVID-19 than before. In turn, this led to increased societal support of hostile government policies towards North Koreans. Conversely, the same participants exhibited higher levels of empathy towards North Koreans during the pandemic, which led to a higher willingness to collaborate with their outgroup. This dual effect on intergroup emotions within intractable conflicts brings forth new avenues from which societies may be able to restrain the destructive influence of the COVID-19 threat on intergroup relations — as well as harvesting its constructive potential for reconciling warring intergroup relations.
... The emotion that participants rated the highest following the emotion induction was selected as their "peak" emotion and subsequently subsumed under four hierarchical emotion categories for study analyses. Categories of fear (afraid, anxious, tense), sadness (empty, lonely, sad, hopeless), and anger (angry, disgusted) were informed by Shaver, et al. 's [41] hierarchical cluster analysis of emotion words. A combined shame/guilt category (ashamed, rejected, guilt) was constructed given some challenges in the literature in regards to conceptual distinctions between these terms (e.g., [42,43]) and indications that these terms are not often verbally differentiated and/ or are often used interchangeably in colloquial language [35]. ...
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Background Theory and research indicate that validation is associated with reductions in negative emotions, whereas invalidation is associated with escalation of negative emotions. However, it remains unclear whether these effects are consistent across emotions, and/or moderated by an individual’s levels of emotion dysregulation. The present study experimentally examines the effects of validation and invalidation across emotions and as moderated by emotion dysregulation. Methods One hundred twenty-six participants completed a measure of emotion dysregulation, and then listened to a rejection-themed imagery script after which they reported the intensity of several emotions. Participants were then presented with either validating or invalidating feedback about their most intense self-reported emotion, depending on their counterbalancing order. They then repeated the procedure for the other condition. Self-reported negative emotions via continuous rating dial, heart rate (HR), and skin conductance level (SCL) were monitored throughout. Results Higher emotion dysregulation was associated with greater increases in self-reported positive emotion when shame or sadness was validated and lesser increases when fear was validated. There were no significant moderating effects of emotion dysregulation in response to invalidation for any emotion on any index. Conclusions The effects of validation appear emotion specific and dependent on levels of emotion dysregulation. These findings may help inform more strategic use of validation in psychotherapeutic interventions.
... ‡ Empath can be utilized to generate lexical categories and contains over 200 built-in, topical and emotional categories generated from common dependency relationships in ConceptNet 22 and Parrot. 23 Topical categories include money, home, work, religion, health, death, etc. Emotional categories include sadness, anger, positive emotion, negative emotion, etc. Terms within both categories were verified using Amazon Mechanical Turk reviewers. ...
Article
One core measure of healthcare quality set forth by the Institute of Medicine is whether care decisions match patient goals. High-quality "serious illness communication" about patient goals and prognosis is required to support patient-centered decision-making, however current methods are not sensitive enough to measure the quality of this communication or determine whether care delivered matches patient priorities. Natural language processing (NLP) offers an efficient method for identification and evaluation of documented serious illness communication, which could serve as the basis for future quality metrics in oncology and other forms of serious illness. In this study, we trained NLP algorithms to identify and characterize serious illness communication with oncology patients.
... 'How many emotions are there?' has always been one of the biggest and the most mesmerizing questions in the field of emotion research. Many emotion researchers have actively suggested core emotions or emotion taxonomy from their own disciplines, such as evolution, neural system, facial expression, physiology, culture (e.g., Osgood, 1966;Izard, 1977Izard, , 1992Plutchik, 1980;Willcox, 1982;Mano and Oliver, 1993;Lee and Lim, 2002;Cowen and Keltner, 2017;Keltner et al., 2019), and language (e.g., Shaver et al., 1987;Storm and Storm, 1987;Hupka et al., 1999;Cowen et al., 2019). The notable points that the studies imply in common are: i) The fixed dimensionality of emotion may not exist, which varies depending on research setting, and ii) The emotion is a complex structure. ...
Preprint
Sentiment analysis that classifies data into positive or negative has been dominantly used to recognize emotional aspects of texts, despite the deficit of thorough examination of emotional meanings. Recently, corpora labeled with more than just valence are built to exceed this limit. However, most Korean emotion corpora are small in the number of instances and cover a limited range of emotions. We introduce KOTE dataset. KOTE contains 50k (250k cases) Korean online comments, each of which is manually labeled for 43 emotion labels or one special label (NO EMOTION) by crowdsourcing (Ps = 3,048). The emotion taxonomy of the 43 emotions is systematically established by cluster analysis of Korean emotion concepts expressed on word embedding space. After explaining how KOTE is developed, we also discuss the results of finetuning and analysis for social discrimination in the corpus.
... Sentiment analysis automatically extracts subjective information from the text and assesses whether someone's view on something is negative, positive, or neutral [25]. Many methods of sentiment analysis have developed [26,27] that can identify more emotions. The preferred automated sentiment analysis method in psychology is the linguistic inquiry and word count (LIWC) [28]. ...
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The rise of cyberbullying has been of great concern for the general public. This study aims to explore public attitudes towards cyberbullying on Chinese social media. Cognition and emotion are important components of attitude, and this study innovatively used text analysis to extract the cognition and emotion of the posts. We used a web crawler to collect 53,526 posts related to cyberbullying in Chinese on Sina Weibo in a month, where emotions were detected using the software “Text Mind”, a Chinese linguistic psychological text analysis system, and the content analysis was performed using the Latent Dirichlet Allocation topic model. Sentiment analysis showed the frequency of negative emotion words was the highest in the posts; the frequency of anger, anxiety, and sadness words decreased in turn. The topic model analysis identified three common topics about cyberbullying: critiques on cyberbullying and support for its victims, rational expressions of anger and celebrity worship, and calls for further control. In summary, this study quantitatively reveals the negative attitudes of the Chinese public toward cyberbullying and conveys specific public concerns via three common topics. This will help us to better understand the demands of the Chinese public so that targeted support can be proposed to curb cyberbullying.
... In cognitive / psychological approaches (which, as mentioned, foregrounds mental state) there are three broad classes of theories that attempt to describe what emotions exist and the interactions between them: categorical, dimensional, and hybrid. Categorical models propose a discrete set of emotions; these include theories by Ekman (1999), Parrott (2001), Shaver et al. (1987), Oatley and Johnson-Laird (1987), and Izard (2007). Dimensional theories propose descriptive dimensions of and relations between emotions, such that experienced or expressed emotions fall along the relevant dimensions and potentially shade into each other, and are not necessarily distinct. ...
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Emotion detection is an established NLP task of demonstrated utility for text understanding. However, basic emotion detection leaves out key information, namely, who is experiencing the emotion in question. For example, it may be the author, the narrator, or a character; or the emotion may correspond to something the audience is supposed to feel, or even be unattributable to a specific being, e.g., when emotions are being discussed per se. We provide the ABBE corpus -- Animate Beings Being Emotional -- a new double-annotated corpus of texts that captures this key information for one class of emotion experiencer, namely, animate beings in the world described by the text. Such a corpus is useful for developing systems that seek to model or understand this specific type of expressed emotion. Our corpus contains 30 chapters, comprising 134,513 words, drawn from the Corpus of English Novels, and contains 2,010 unique emotion expressions attributable to 2,227 animate beings. The emotion expressions are categorized according to Plutchik's 8-category emotion model, and the overall inter-annotator agreement for the annotations was 0.83 Cohen's Kappa, indicating excellent agreement. We describe in detail our annotation scheme and procedure, and also release the corpus for use by other researchers.
... Amongst such divergent analyses, three key models are commonly incorporated, namely Ekman's theory of basic emotions (Ekman 1992), Plutchik's wheel of emotion (Plutchik and Kellerman 2013), and Russel's circumplex model (Russell 1980). Further, other emotion modelling frameworks were also proposed in the literature such as Shaver (Shaver et al. 1987), Oatley (Cambria et al. 2013), OCC (Ortony, Clore, and Collins 1990), VAD (Verma and Tiwary 2017), and Lovheim (Lövheim 2012). The reader can refer to (Kim and Klinger 2018) and (Alqahtani and Alothaim 2022) to obtain further information on these models. ...
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The development of emotion detection technology has emerged as a highly valuable possibility in the corporate sector due to the nearly limitless uses of this new discipline, particularly with the unceasing propagation of social data. In recent years, the electronic marketplace has witnessed the establishment of a large number of start-up businesses with an almost sole focus on building new commercial and open-source tools and APIs for emotion detection and recognition. Yet, these tools and APIs must be continuously reviewed and evaluated, and their performances should be reported and discussed. There is a lack of research to empirically compare current emotion detection technologies in terms of the results obtained from each model using the same textual dataset. Also, there is a lack of comparative studies that apply benchmark comparison to social data. This study compares eight technologies; IBM Watson NLU, ParallelDots, Symanto-Ekman, Crystalfeel, Text to Emotion, Senpy, Textprobe, and NLP Cloud. The comparison was undertaken using two different datasets. The emotions from the chosen datasets were then derived using the incorporated APIs. The performance of these APIs was assessed using the aggregated scores that they delivered as well as the theoretically proven evaluation metrics such as the micro-average of accuracy, classification error, precision, recall, and f1-score. Lastly, the assessment of these APIs incorporating the evaluation measures is reported and discussed.
... The fine-to-coarse cluster relationship of these human affect categories follows the human affect tree structure proposed by Shaver et al. [37]. The test set C novel therefore represents more fine-grained human affects. ...
... Ekman 1992Ekman , 1999Plutchik 1980aPlutchik , 1980bPlutchik , 2001) and on the emotions that were regularly represented in the poems of our corpus. We categorized the emotions into 6 groups, inspired by the emotion hierarchy in (Shaver et al. 1987). First, each poem was annotated independently by two annotators, then they merged annotations manually into a consensus annotation. ...
Conference Paper
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This paper is about how to use quantitative methods to study emotions in literary texts and how to describe the differences between literary periods in terms of the representation of emotion. Our use case is the difference between realism and early modernism in German literary history and we are focusing on poetry. In a first step a group of domain experts manually annotated around 1.000 poems, highlighting phrases according to the emotions they represented. In a second step a machine learning model was trained and in a third step this model was used to annotate a collection of more than 6.000 poems from anthologies representing either realism or early modernism. Lastly we analyzed the main differences of these periods based on the trends we found.
... Consequently, individuals have developed psychological disorders, such as anxiety, fear, depression, and helplessness (Ahorsu et al., 2020;Guo et al., 2020;Hacimusalar et al., 2020). Emotions are grouped into three distinct categories: primary, secondary, and tertiary (Shaver et al., 1987). Fear and anxiety are two negative emotions that affect decision-making. ...
Article
To develop tourism products and campaigns that will not only help the tourism industry to survive, but also revive and sustain it in the future, it is imperative to understand the behavioral intentions of leisure travelers during the coronavirus disease (COVID-19) pandemic. This study aims to explore how Japanese traveler segments are characterized by their attitudes and feelings toward domestic travel during the COVID-19 pandemic. A market research study was conducted on Japanese nationals, using 1353 questionnaires in the analysis. A factor analysis was generated in six attitude dimensions: 1) COVID-19 Anxiety, 2) Government Mistrust, 3) Psychological Impact, 4) Optimistic Outlook, 5) Financial Impact, and 6) Social Anxiety; through K-means cluster analysis, the segmentation produced three distinct tourist segments: Confident Travelers, Anxious Travelers, and Social Travelers. Each segment is clearly described in terms of attitudes, considering distinct socio-demographic characteristics. Practical suggestions on how to manage and target these segments include implementing travel packages with minimal face-to-face contact for anxious travelers, travel subsidies through national campaigns for confident travelers, or conventional marketing campaigns for social travelers.
... A dream is defined as an emotion of need or an expectation for something that is missing, described as the need for something-a thing, a state, or a relationship [23,24]. As a combination of emotions, different kinds and levels of dreams that have an important impact on an individual's psychological development, interpersonal relationships, and action tendencies [25][26][27]. Based on this, this paper introduces the concept of entrepreneurial dreams, which are defined as the emotions and desires of individuals regarding the need or expectation of entrepreneurial behavior. ...
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Many people have entrepreneurial dreams in mind, yet existing research has neglected to focus on this phenomenon. This paper introduces the concept of entrepreneurial dreams, constructs a model of the relationship between entrepreneurial dreams and turnover intention to start-up, based on identity theory and prospect theory, and empirically analyses the mechanism of the effect of entrepreneurial dreams on turnover intention to start-up. Through the analysis of data from two multi-provincial and multi-wave employee studies (Study 1 N = 198, Study 2 N = 227), the findings show that: (1) employees’ entrepreneurial dreams positively influence turnover intention to start-up; (2) employees’ entrepreneurial dreams can stimulate employees’ sense of entrepreneurial self-efficacy, thus positively influencing turnover intention to start-up; (3) job embeddedness plays a moderating role in the relationship between entrepreneurial self-efficacy and turnover intention to start-up, specifically, the higher the degree of job embeddedness, the weaker the effect of entrepreneurial self-efficacy on turnover intention to start-up; (4) job embeddedness moderates the indirect effect of entrepreneurial dreams on turnover intention to start-up through entrepreneurial self-efficacy, specifically, the higher the degree of job embeddedness, the weaker the indirect effect of entrepreneurial dreams on turnover intention to start-up through entrepreneurial self-efficacy. This study reveals the mediating role of employees’ entrepreneurial self-efficacy and the moderating role of job embeddedness in the influence of entrepreneurial dreams on employees’ turnover intention to start-up, which provides theoretical and practical references for relevant organizations.
... Emotions Approach Ekman [36] 6 Categorical Shaver et al. [37] 6 Categorical Oatley et al. [38] 5 Categorical Plutchik [15] 32 Wheel Circumplex Russell [39] 28 Dimensional OCC -Ortony et al. [40] 22 Dimensional Lovheim [41] 8 Dimensional ...
Conference Paper
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Emotion detection is crucial in many IoT deployments from an operational perspective with examples ranging from digital health to smart cities. This is particularly true in smart homes where the interaction between the local IoT ecosystem and the inhabitants are continuous, pervasive, and nuanced. More specifically, emotion estimation from human speech attributes is an integral architectural component of such ecosystems. In this work, we survey the emerging LPWAN technologies and after selecting the most optimal for our use-case, we propose an emotion estimation scheme based on LoRa wireless technology for automated actions in smart home environments. In particular, a voice recognition module coupled with a transmitter are installed in a car. Then, depending on the estimation outcome, the smart home may undertake one or more preemptive actions according to its configuration as the car passenger approaches. The prototype LoRa system has been tested with the widely-used TESS dataset with encouraging results as expressed in the emotion confusion matrix obtained from a extreme learning machine with various kernels. The preceding paves the way for adaptive smart homes tailored to the needs of their inhabitants.
... Desire is also posited to be an outcome of emotional benefits (Strahilevitz & Myers, 1998). Researchers used emotional model to describe and measure types of emotions which includes primary, secondary and tertiary emotions (Shaver et al., 1987;Parrott, 2001). Robert Plutchik (1980) has conceptualized emotional benefits at an individual level. ...
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This study focused on how and whether cause campaign affects emotional benefits as well as trust of consumers in the sustainability of organic cosmetics. It examines the relevance of emotional benefits as a mediating variable between cause campaign and trust in the sustainability of organic cosmetics. While this study found evidence of cause span effect on emotional benefits and trust, there was also evidence of the strong mediation effect of cause purview on trust. The findings also indicate that the model is valid and has good fit. The relative predictive relevance of the model has high impact on the cause span and trust. This research endeavour is significant because it has several implications for trust, with regard to sustainability of organic cosmetics which is conceptualized and operationalized at an individual level.
... Facial expressions are frequently described in terms of a small number of universal categories, such as anger, disgust, fear, happiness, sadness, and surprise (Ekman & Friesen, 1971;Ekman et al., 1969). Theories of emotion recognition based on these putative categories generally assume that facial images are evaluated for similarity to a prototype expression for each particular category, leading to the assignment of a category label based on maximum similarity (Fehr & Russell, 1984;Rosch 1999;Shaver et al., 1987). In the strictest version of such an account, any facial expression would be assigned one of the basic emotion labels, with images near the boundaries of category regions being especially sensitive to noise that could lead to variable categorization across observers or across multiple recognition opportunities. ...
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Categorical perception involves our perceptual system creating sharp boundaries along an objectively continuous stimulus property, such as the discrete colors of the rainbow being perceived despite continuous change in wavelength. The same mechanism is thought to take place in facial emotion perception. But how are emotions at these boundaries perceived? We presented participants with morphed emotional faces made by blending different emotional expressions in equal proportions. Next, we asked participants to respond freely to these ambiguous face morphs and examined these responses via natural language processing methods. The results showed that participants used many more labels than those related to the categories which went into the morphs. These results can inform theories on categorical facial perception as well as the mental representation of facial expressions.
... Emotion plays an important role in human language to show a certain perception of a condition or situation. Furthermore, it plays a central role in individual experiences [8] , such as the experience of shopping online. In their research [9] , Shaver et al. defined five basic-level emotion categories, i.e., love, happiness, anger, fear, or sadness. ...
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Recognizing emotions is vital in communication. Emotions convey additional meanings to the communication process. Nowadays, people can communicate their emotions on many platforms; one is the product review. Product reviews in the online platform are an important element that affects customers’ buying decisions. Hence, it is essential to recognize emotions from the product reviews. Emotions recognition from the product reviews can be done automatically using a machine or deep learning algorithm. Dataset can be considered as the fuel to model the recognizer. However, only a limited dataset exists in recognizing emotions from the product reviews, particularly in a local language. This research contributes to the dataset collection of 5400 product reviews in Indonesian. It was carefully curated from various (29) product categories, annotated with five emotions, and verified by an expert in clinical psychology. The dataset supports an innovative process to build automatic emotion classification on product reviews.
... In this sense, emotions can provide a way for connecting people who belong to different groups, intended as culture, age, education, and different sensory characteristics. The expression of emotions through language, in particular, lies at the basis of several models of emotions, including Shaver's ( [24]) and Plutchik's ( [20]), and has prompted the creation of a number of resources for sentiment analysis ( [4,22,26]). The application of these resources to art is straightforward: for example, WikiArt Emotions ( [17]) is a dataset of 4,105 artworks from WikiArt annotated for the emotions evoked in the observer. ...
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The paper presents a combined approach to knowledge-based emotion attribution and classification of cultural items employed in the H2020 project SPICE. In particular, we show a preliminary experimentation conducted on a selection of items contributed by the GAM Museum in Turin (Galleria di Arte Moderna), pointing out how different language-based approaches to emotion categoriza-tion (used in the systems Sophia and DEGARI respectively) can be powerfully combined to cope with both coverage and extended affective attributions. Interestingly, both approaches are based on an ontology of the Plutchik's theory of emotions.
... Researchers from different fields have a unique understanding of emotion models. Most emotions are classified as either having a positive or negative valence [64]. Latinjak's model [65] defines 20 emotions, including four neutral emotions: Alerted, Calm, Surprise, and Fatigued. ...
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Multiview subspace learning (MSL) has been widely used in various practical applications including emotion recognition. Despite the recent progress in MSL, two challenges remain to address. First, most existing MSL methods indiscriminately utilize both helpful and defective information contained in different views. Second, the most recent methods are linear approaches that do not perform well on emotion datasets with weak linear separability. Therefore, in this study, we introduce a framework for emotion recognition: multiview nonlinear discriminant structure learning (MNDSL). MNDSL fully exploits useful information in each input through local information preservation and discriminant reconstruction (LPDR) and obtains latent subspaces using multiview discriminant latent subspace learning (MDLSL). In addition, an out-of-sample extension was introduced to satisfy the requirements of large-scale applications and obtain the projections of new samples. The proposed framework constructs interviews and intra-view-weighted connections to explore discriminant structures and preserve locality under complementarity and correlation principles. The results demonstrate the superiority of the proposed framework compared with state-of-the-art methods.
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Consumers often receive unfair treatment during service encounters, such as receiving less monetary compensation than peers, thus inducing perceptions of unfairness. However, no research has yet shown how this perceived unfairness affects consumers' likelihood of choosing unique products. We propose that when consumers feel that they are being unfairly treated in comparison with their group peers, they are more likely to seek unique products. Need for social status is proposed to mediate the above process. Perceived economic mobility is suggested to moderate this effect so that consumers with high (vs. low) economic mobility perception are more (vs. less) likely to choose unique products after being treated unfairly. We demonstrate these effects with six studies. Studies 1a and 1b reveal a positive correlation between chronic fairness deprivation and desire for unique products. Study 2a demonstrates the causal relationship between primed unfairness and desire for unique products and study 2b tests the effects of a set of potential confounds. Study 3 examines need for social status as the underlying mechanism. Study 4 further explores perceived economic mobility as the boundary condition in the above relationship. Companies can benefit from our findings by providing unique products or services to restore consumers' status and buffer negative feelings during such service failures.
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Videogames evoke emotions that have implications for in-game performance and enjoyment. However, no measure currently exists to assess discrete emotions in videogame contexts with evidence of validity. The current study tested the factorial and construct validity of responses obtained with a modified version of the Discrete Emotions Questionnaire (DEQ, Harmon-Jones et al., 2016) and tested measurement invariance across player-versus-player-oriented and player-versus-environment-oriented videogame types (DEQ-VG). To ensure the factor structure held across both positive and negative emotional experiences, a total of 2994 participants were asked to recall one positive and one negative emotional experience stemming from a videogame they had recently played and completed the DEQ-VG in reference to each recalled experience. Separate confirmatory factor analyses were conducted for the two recalled emotional events to assess factorial validity. Construct validity was assessed by comparing DEQ-VG scores between positive and negative emotional events. The results supported a 9-factor solution (anger, happiness, fear, excitement, sadness, relaxation, desire, anxiety, and awe), and the responses were invariant across game types. Construct validity was demonstrated by the DEQ-VG scores significantly differing in the expected directions between positive and negative events. These findings support the usefulness of the DEQ-VG for assessing discrete emotions stemming from videogame experiences.
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“Anti-chicken soup” (ACS) persuades people to yield to reality and give up rather than encouraging people to work hard as “chicken soup” does. The current study explored whether people with a higher level of relative deprivation (RD) would be more likely to endorse ACS. We found that people with high-measured (Study 1) and manipulated (Study 2) RD were more likely to endorse ACS. Study 2 also suggested that the effect was mediated by self-handicapping. It seems that relatively deprived individuals may adopt the strategy of self-handicapping so that they could attribute their failure to external causes, which in turn leads to lower motivation to try their best and ultimately the endorsement of ACS.
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Online social networking has become a popular means of information exchange and social interactions. Users of these platforms generate massive amounts of data about their relationships, behaviors, interests, opinions, locations visited, items purchased, and subjective experiences of various aspects of life. Moreover, these platforms enable people from wide-ranging social and cultural backgrounds to synergize and interact. One interesting area of research is the emotional dimensions contained in this user-generated content, specifically, emotion detection and prediction, which involve the extraction and analysis of emotions in social network data. This study aimed to provide a comprehensive overview and better understanding of the current state of research regarding emotion detection in online social networks by performing a systematic literature review (SLR). SLRs help identify the gaps, challenges, and opportunities in a field of study through a careful examination of current research to understand the methods and results, ultimately highlighting methodological concerns that can be used to improve future work in the field. Hence, we collected and analyzed studies that focused on emotion in social network posts and discussed various topics published in digital databases between 2010 and December 2020. Over 239 articles were initially included in the collection, and after the selection process and application of our quality criteria, 104 articles were examined, and the results showed a robust extant body of literature on the text-based emotion analysis model, while the image-based requires more attention as well as the multiple modality emotion analysis.
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Objectives Social ties increase in importance in late life and narcissism may be deleterious to these ties. More narcissistic older adults may have more frequent social encounters than less narcissistic people and may prefer weak ties (e.g., acquaintances) over close ones (e.g., family, close friends). They may benefit more from these encounters due to their need for adulation. This study examined how daily social experiences and mood varied by narcissism among older adults. Methods Older adults aged 65–92 (N = 303) completed the Narcissistic Personality Inventory-16 and completed ecological momentary assessments in which they reported number, type, and quality of social contacts and positive and negative mood every 3 hours for 5 to 6 days. Results In multilevel models, narcissism did not predict the number or pleasantness of social encounters. But more narcissistic older adults reported a greater percentage of stressful discussions with weak ties. With regard to mood, more narcissistic people reported higher negative mood if they had more encounters with weak ties and when discussing something stressful with weak ties. Less narcissistic people reported lowered positive mood after they discussed something stressful with close ties. Discussion The findings present a nuanced understanding of how the self-centeredness of narcissism may be manifest in late life. More narcissistic people may be less sensitive to close partner’s (e.g., family, friends) distress but their mood may be more susceptible to negative social events, especially with weak ties (e.g., acquaintances).
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The COVID-19 pandemic has had a wide range of negative physical and mental impacts. This review begins with a theoretical explanation of the psychological defense mechanisms used to deal with the pandemic. It then discusses different categories of defense mechanisms and their roles in managing the impacts of psychological distress. The aim of this review is to highlight the various psychological defense mechanisms individuals use to deal with the pandemic and to discuss how adjustment mechanisms can protect individuals from internal and external threats by shielding the integrity of the ego (the mind) and helping individuals maintain their self-schema.
Thesis
Résumé : La réalité virtuelle (RV) réunit les plus grandes marques et les consommateurs dans le métavers pour vivre des expériences de consommations ludiques sur le « web 4.0 », révolutionnant l’expérimentation et la découverte d’offres. En interagissant physiquement, le consommateur devient l’acteur principal, au plus près de l’expérience réelle. L’objectif de la thèse est de comprendre dans quelle mesure les composantes expérientielles influencent ce sentiment d’incarnation et quels sont les effets de ce dernier sur l’attitude et le comportement du consommateur. La démarche de recherche hypothético-déductive nous amène à construire un cadre théorique en marketing expérientiel puis d’explorer le phénomène par des études qualitatives à visées exploratoires afin d’élaborer un modèle conceptuel. Le modèle intègre la position du corps (assis vs. debout) et les défis (absence vs. modérés vs. avancés) tels les déterminants de l’incarnation ainsi que l’immersion et le plaisir tels les facteurs explicatifs de l’incarnation et des intentions comportementales. L’étude quantitative (n=328) est analysée par un modèle d’équations structurelles PLS. Six unités expérimentales composent le contexte expérientiel relatif la visite d’une destination touristique virtualisée. Les résultats confirment le rôle des effets prédictifs ainsi que ceux joués par des médiateurs et modérateurs entre certaines relations du modèle conceptuel. La recherche présente les apports méthodologiques et théoriques et formule des préconisations à destination des professionnels et de chercheurs dans le cadre de futures recherches.
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Despite research suggesting that emotional interactions pervade daily resource exchanges between leaders and members, the leader‐member exchange (LMX) literature has predominantly focused on the interplay between general affective experiences and the overall relationship quality. Drawing upon the affect theory of social exchange, we examine why and how discrete exchange imbalance engenders distinct emotions and shapes downstream work behaviors of the members. Results from a preregistered experimental study with 247 participants and an experience sampling study with time‐lagged reports from 79 leaders and 145 members show that a positively imbalanced exchange increases members’ subsequent leader‐directed helping via gratitude (but not via shame) and that a negatively imbalanced exchange increases members’ subsequent risk taking via pride (but not via anger). Moreover, the intensity of such effects hinges upon the average level of resource contributions of leader‐member dyads. Our research casts light on the role of transient emotions in dynamic resource exchanges between leaders and members and enriches our knowledge of within‐dyad fluctuations of social exchanges. This article is protected by copyright. All rights reserved
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In recent year, Authors had been attempting to find or detect the feeling of human by recorded brain signal for example, EEG (electroencephalogram) alerts. Because of the unnecessary degrees of unwanted signal from EEG recording, a solitary feature alone can't accomplish great execution. Distinct feature is key for automatic feeling identification. Right now, we present an AI based scheme utilizing various features extricated from EEG recordings. The plan joins these particular highlights in feature space utilizing both managed and unaided component choice procedures. To re-request the joined highlights to max-importance with the names and min-repetition of each feature by applying Maximum Relevance Minimum Redundancy (MRMR). The produced highlights are additionally diminished with principal component analysis(PCA) for removing essential segments. Test report will be generated to show that the proposed work should outperform the condition of-workmanship techniques utilizing similar settings in real time dataset.
Thesis
Notre étude développe les théories de trois grandes figures qui ont marqué la recherche scientifique des émotions, William James en psychologie, Joseph LeDoux en neurobiologie et Antonio Damasio en neuropsychologie, afin d’établir des comparaisons avec la psychophysiologie cartésienne des passions. Il s’agit ainsi non seulement de revisiter la troisième notion primitive cartésienne pour ouvrir la discussion avec les neurosciences, mais également de proposer une nouvelle façon d’établir des comparaisons à l’aide d’homologies fonctionnelles. Nous examinons alors, conformément à cette méthode, les phénomènes qui fondent le processus émotionnel cartésien, pour les rapprocher fonctionnellement de phénomènes expliqués de nos jours par les neurosciences des émotions. Ces comparaisons nous conduisent à révéler dans la psychophysiologie cartésienne des moments « cognitivistes » qui semblent à première lecture s’opposer au discours « somatique » de fond. Cela nous conduit alors à la fois à penser la cohérence du discours cartésien et à montrer que l’entreprise des neurosciences est aussi traversée par cette dualité. Ces homologies révèlent que la subtilité du texte cartésien permet de mettre en lumière des espaces conceptuels minimisés par les sciences cognitives sur des questions de fond comme l’explication de la conation ou le déclenchement de la volonté dans l’esprit. À l’inverse, les explications neuropsychologiques de certains phénomènes cognitifs peuvent révéler la faiblesse ou les incohérences du modèle cartésien pour livrer une explication satisfaisante de ces phénomènes. Ces homologies apportent donc au philosophe des opportunités d’interprétations nouvelles du corpus cartésien à l’aide de grilles de lecture originales fournies par les neurosciences.
Chapter
The evolution to having emotion metrics in product design is a growing body of research. This chapter provides an overview regarding why it is valuable to understand the current array of emotion theories in consumer and product research. Examples of direct and indirect approaches to uncovering potential emotional connections within the research context are provided. The authors review key emotion theories and a sample of tools that try to identify emotions and their role in human behavior raging from common definitions for Affect, Mood, Emotion, Attitude, and Emotive, Dimensional Theories on emotion, Appraisal theory and Critical Incident Technique, Emotion regulation and coping, and Social media and emotion.
Chapter
Indeed, due to the rapid advances of the times and the development of the Internet, the way in which marketing is addressed today has changed and consumers have the opportunity to “generate” their own information about products and brands, as well as to spread and share it through copying, browsing and etc. Consumers 'purchasing decisions are more susceptible, as the increase in ways of gaining and generating multitudinous information and some might be deceptive—because of the decreasing cost of sharing, the content and quality of consumer-generated marketing messages are becoming increasingly difficult to control as a result. Emotion acts as one of the factors contributing to changes in behaviors and actions, has been widely studied in the past few decades [9]. Realistically, the marketing messages that are spread by consumers seem to share a common characteristic of trying to achieve communication by evoking emotional resonance in consumers. We found that emotion will not only help to explore and reveal the relationship between the emotionality of the message content and the sharing behavior but also provides more valuable and relevant suggestions and insights for enterprises to carry out new media marketing, as it distinguishes marketing messages from ordinary messages [2].KeywordsEmotional marketingBusiness managementCoca-Cola
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The paper introduces mathematical encoding for subjective experience and meaning in natural cognition. The code is based on a quantum-theoretic qubit structure supplementing classical bit with circular dimension, functioning as a process-causal template for representation of contexts relative to the basis decision. The qubit state space is demarcated in categories of emotional experience of animals and humans. Features of the resulting spherical map align with major theoreties in cognitive and emotion science, modeling of natural language, and semiotics, suggesting several generalizations and improvements. The developed model bridges psychological, quantum-theoretic, and semiotic perspectives, allowing for an integrative account of subjectivity, agency, and meaning in living Nature. Graphical abstract
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Emotional words differ in how they acquire their emotional charge. There is a relevant distinction between emotion-label words (those that directly name an emotion, e.g., “joy” or “sadness”) and emotion-laden words (those that do not name an emotion, but can provoke it, e.g., “party” or “death”). In this work, we focused on emotion-label words. These words vary in their emotional prototypicality, which indicates the extent to which the word refers to an emotion. We conducted two lexical decision experiments to examine the role played by emotional prototypicality in the recognition of emotion-label words. The results showed that emotional prototypicality has a facilitative effect in word recognition. Emotional prototypicality would ease conceptual access, thus facilitating the retrieval of emotional content during word recognition. In addition to the theoretical implications, the evidence gathered in this study also highlights the need to consider emotional prototypicality in the selection of emotion-label words in future studies.
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There are several good reasons for studying children’s insight into their emotional processes. We occasionally think of an emotional reaction as being more-or-less involuntary, but it is obvious that, in certain respects, we can exercise control over our emotions. We can be taught to hide our emotions or we can be taught to exaggerate them. Indeed, some cultures expect their members to inhibit displays of emotion, and others encourage lavish displays. These cultures presumably teach their children both explicitly and implicitly what is acceptable. We also, as most psychotherapies presuppose, try to exercise control, not just over the outward display of our feelings but over the actual experience of emotion. We put anxiety-provoking thoughts out of our mind. Alternatively, we dwell on an emotionally charged episode in an effort to prolong the emotion. Thus, both our outward display of emotion and our emotional experience can be redirected and controlled. By analogy with the development of memory (Flavell & Wellman, 1977), therefore, we might expect the developing child to gradually acquire insight into such strategies for self-control and put them into practice.
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