Emotional Words Research Articles

Діалог представників служб першої допомоги з травмованим комбатантом в умовах фронту: конструювання комунікативного тренінгу

Background. Communicative studies in professional discourse are highly effective when applied through practical workshops and training sessions. Modern education programs must incorporate a communicative component when developing professional training curricula. Researchers, drawing from over 70 years of focused efforts to alleviate the traumatic experiences of military conflicts, have compiled valuable materials from the works of linguists, psychotherapists, military medics, and chaplains. This compilation forms the basis for modeling professional dialogues with traumatized combatants and preventing misunderstandings of their emotional states. M e t h o d s. The analysis of global practices in training first aid services and combatants shows that military training should include exercises in using emotional language in first aid dialogues. The communicative training was developed using methods such as abstraction and logical generalization, surveys, reports, personal interviews, and testimonies from combat participants. Empirical data collection was ensured through observation and conversational analysis, based on the concepts of J. Heritage and S. Clayman. Results. Data from 14 combatants who are or were in the combat zone during the full-scale Russian invasion of Ukraine from March 2022 to March 2024, compared with data from previous research since 2015, show that accurate interpretation of emotional language is crucial for successful professional dialogue. By identifying the main types of emotions and states characteristic of combatants at the moment of trauma, the authors identified three types of first professional aid dialogues: algorithmization, refocusing, and facilitation. A communication training program was designed for these dialogues, including stages of analyzing the communicative situation and emotional signals experienced by the affected person, selecting language units, constructing responses, testing the dialogue, and reflecting on the process. Conclusions. The research presented in this article recommends organizing communicative training, particularly in military academies in Ukraine. The training plan provides an educational foundation for developing dialogical skills in military personnel, such as tracking and expressing emotional reactions, building trust, eliciting positive emotional responses from the interlocutor, and using linguistic techniques to establish emotional interaction with traumatized combatants and encourage their participation in the dialogue.

Mechanisms Driving Online Vaccine Debate During the COVID-19 Pandemic

The prevalence of the anti-vaccine movement in today’s society has become a pressing concern, largely amplified by the dissemination of vaccine skepticism. During the early stages of the COVID-19 pandemic, the vaccination debate sparked controversial debates on social media platforms such as Twitter, which can lead to serious consequences for public health. What determines anti-vax attitudes is an important question for understanding the source of the campaigns and mitigating the misinformation spread. Compared with other countries, Türkiye differentiates itself with high vaccination rates and lack of political support for anti-vaxxers despite its highly polarized political system. Analyzing Turkish Twittersphere, we explore several mechanisms capturing content production and behaviors of accounts within the pro- and anti-vax segments in online vaccine-related discussions. Our findings indicate there is no relation between political stance and anti-vaccine attitude. Both supporters of vaccination (pro-vaxxers) and opponents (anti-vaxxers) can be found across the political spectrum. Moreover, linguistic differences reveal that anti-vaxxers employ more emotional language, while pro-vaxxers express more skepticism. Notably, automated accounts are less prevalent leading to difficulty in assessing genuine support for vaccines, while anti-vaccine bots produce slightly more content. These findings have crucial implications for vaccine policy, emphasizing the importance of understanding diverse language patterns and beliefs among anti-vaxxers and pro-vaxxers to develop effective communication strategies at the national level.

Emotional Video Captioning With Vision-Based Emotion Interpretation Network.

Effectively summarizing and re-expressing video content by natural languages in a more human-like fashion is one of the key topics in the field of multimedia content understanding. Despite good progress made in recent years, existing efforts usually overlooked the emotions in user-generated videos, thus making the generated sentence a bit boring and soulless. To fill the research gap, this paper presents a novel emotional video captioning framework in which we design a Vision-based Emotion Interpretation Network to effectively capture the emotions conveyed in videos and describe the visual content in both factual and emotional languages. Specifically, we first model the emotion distribution over an open psychological vocabulary to predict the emotional state of videos. Then, guided by the discovered emotional state, we incorporate visual context, textual context, and visual-textual relevance into an aggregated multimodal contextual vector to enhance video captioning. Furthermore, we optimize the network in a new emotion-fact coordinated way that involves two losses- Emotional Indication Loss and Factual Contrastive Loss, which penalize the error of emotion prediction and visual-textual factual relevance, respectively. In other words, we innovatively introduce emotional representation learning into an end-to-end video captioning network. Extensive experiments on public benchmark datasets, EmVidCap and EmVidCap-S, demonstrate that our method can significantly outperform the state-of-the-art methods by a large margin. Quantitative ablation studies and qualitative analyses clearly show that our method is able to effectively capture the emotions in videos and thus generate emotional language sentences to interpret the video content.

Piano Performance Techniques and Musical Expressiveness

Music, often described as the universal language of emotions, transcends cultural boundaries and speaks to the deepest recesses of the human soul. Among the myriad of musical instruments, the piano holds a special place, capable of conveying a vast range of emotions and moods. Central to the art of piano playing are the techniques employed by pianists to bring forth both technical brilliance and profound emotional expression. This paper delves into the intricate relationship between piano performance techniques and the art of conveying musical expressiveness. By examining the synergy between these two elements, this study aims to unravel the nuances that govern piano performance and explore how musicians imbue their interpretations with personal creativity and sentiment.

An ERP Study on Affective Priming of Second Language: The Emotion Word Type Effect in Unmasked and Masked Priming Paradigms

An ERP Study on Affective Priming of Second Language: The Emotion Word Type Effect in Unmasked and Masked Priming Paradigms

Constructing a Theory of Affective Stylistics in Modern Chinese Literature Based on Knowledge Mapping

Abstract Emotion stylistics is an important bridge to communicate the emotions of characters and the emotions of readers in modern Chinese literature. This paper extends the emotion dictionary based on the point mutual information in the SOPMI algorithm and introduces the emotion words into the emotion dictionary to calculate the emotion values of modern Chinese literature texts. Modern Chinese literature’s emotions can be visualized through knowledge filtering, knowledge fusion, and text classification modules of the knowledge graph. Multimodal theory is used to construct a theoretical framework that is dynamic and multimodal stylistic. The study focuses on the emotional intensity, tendency, flow, and visualization analysis of The Divine Eagle Warrior, a modern Chinese literature, as the research object. The results show that the emotional density of the original work is 0.107, the amount of both positive and negative emotions reaches about 3, and the maximum fluctuation of emotions in the whole text is 0.23. The results reveal the structural composition of emotions in the discourse of modern Chinese literature, which is of great reference significance for further understanding the nature of literature and constructing the theory of emotional stylistics.

Exploring the Direction of English Translation of Environmental Articles Based on Emotional Artificial Intelligence Learning Models

Abstract In this paper, we improve the performance of machine translation emotion recognition by fusing word features and linguistic features of environmentally friendly text with LSTM neural networks. Multi-channel LSTM’s unbalanced emotion classification method can address the problem of unbalanced numbers of samples in each emotion category in the emotion classification task. The bilingual semantic similarity degree feature is added to the translation process of English environmental protection data so as to make the translation of English environmental protection data more accurate. The proposed method is validated through experiments, and the results show that the translation quality of the LSTM model is generally higher than that of other models, and the mean value of the translation quality of the LSTM on the test17 dataset is 50.4. In terms of environmental protection terminology, mistranslations, common word mistranslations, etc. There is a significant reduction of 2–3 times in the absolute number of the LSTM model compared to the traditional method. A total of 94,788 corpora were obtained from 10 environmental translations, such as Silent Spring and Solace of the Heart, and 2,311 favorite emotion words were identified. Comparative analysis of the samples reveals that most of the translation results of some environmental terms are in the range of 0.7–0.9, and the range of semantic overlay between the machine translation results of the LSTM model and the standard translations is large. Most users believe the model improves the translator’s translation level and diction accuracy.

Machine Learning Based Analysis and Prediction of Emotional Expressions in Dance Movements

Abstract Dance movement is a powerful way to convey human emotions, and analyzing and predicting the emotional expression of dance movement through machine learning has become a hotspot in the field of artificial intelligence research nowadays. This paper employs the Seagull optimization algorithm to enhance the SVM classification model, laying the algorithmic groundwork for the research, and refines it to align with the research requirements. The classification of dance movements is accomplished by the nonlinear regression algorithm in the support vector machine regression algorithm, while the task of capturing dance movements is realized by means of Euler angles to describe the orientation, rotation matrices to transform vectors between different coordinate systems, and quaternions to optimize the Euler angles. Finally, this paper analyses and predicts the emotional expression of dance movements using a classification loss model (LSTMBO) and a W-RNN model that incorporates the weights of emotion words. In this paper, tests on the classification algorithm revealed that the algorithm's classification accuracy is above 90% for all datasets used in the research. Moreover, the performance and effectiveness of dance action capture are significantly better than other comparative algorithms. Simultaneously, this paper's algorithm achieves an accuracy of over 80% in predicting emotions expressed through dance movements. Numerous experiments have proven the effectiveness and superiority of the algorithm model in this paper, thereby promoting the research and development of the field.

Integrative Development of Modern and Contemporary Literary Works and Traditional Culture Combined with Semantic Association Network Modeling

Abstract In recent years, the development of network novels has pushed traditional cultures such as Taoism, Buddhism, and Confucianism to a peak of attention, making traditional cultures constantly emphasized and excavated, forming an important social subject. The evaluation of textual entities with respect to the integration of modern and contemporary literary works and traditional culture is supported by a semantic association network model proposed in this paper. The model fully exploits the heterogeneity of semantic associations between modern and contemporary literary works and traditional culture and utilizes the RSS model to extract the emotional words of traditional cultural elements in the text of literary works in order to decide the global importance of traditional cultural elements in literary works. Finally, the effectiveness of the method was verified in the dataset. The results of the study show that in the dataset of modern and contemporary literary works, the traditional cultures with the highest semantic association strengths are Taoist culture (0.657), Confucian culture (0.583), and folk culture (0.651), respectively. The incorporation of traditional culture by writers in their literary works can result in a mutual achievement and development of literary works and traditional culture.

Apocalypse now or later? Nuclear war risk perceptions mirroring media coverage and emotional tone shifts in Italian news

Abstract This study delves into the progression of nuclear war risk perceptions during the initial 6 months of the Ukraine war. It particularly investigated the influence of Italian media coverage changes and the affective tone of war representation. Utilizing a mixed-methods approach, two separate yet interconnected studies were conducted. The first study employed web scraping and keyword selection techniques to assess emotional language and quantify war-related content in the headlines of Italian online newspapers from March to July 2022. Results demonstrated a linear decrease in war-related news and an emotional shift, with a significant decrease in fear and an increase in joy noted between March and May. The second study examined nuclear war risk perceptions at an individual level, surveying a panel of 397 Italians at three distinct points during the same time frame. The findings revealed a similarity between the media’s affective tone and individuals’ affective risk perceptions. Analytic risk perception, in contrast, showed a linear decrease that matched the decline in war-related news volume. The study found preexisting individual differences, among women and older participants, to be significant determinants in shaping risk perception evolution. These groups exhibited higher initial risk perceptions and more resistance to change as the scenario unfolded. This research contributes to the existing body of work that underscores the media’s role in influencing risk perceptions by illuminating the relationship between media representation of the Ukraine war and individual-level affective risk perception. Furthermore, it highlights individual differences as significant moderators of risk perception change during a crisis.

Emotional Characterization Mining in Music Performance and Its Guiding Role

Abstract Emotional attributes are crucial in music performance, serving a pivotal role in guiding interpretation and execution. This study employs a Wavenet layer within a Waveform-Deep Neural Network (WLDNN) to extract emotional features from musical performances. An activation function is then applied to process and refine these features. Additionally, a Generative Adversarial Network (GAN) is utilized to identify and remove irrelevant features, thereby enhancing the quality of the emotional attributes extracted. The evaluation of these emotional features employs both music emotion language values and an emotion vector model. Our analysis demonstrates that this methodology achieves a 90% accuracy rate in identifying and extracting emotional features from music performances. Based on these findings, a stage lighting control system was developed, tailored to respond to the emotional cues within the music. The system was tested across ten different performances with a sampling frequency of 5 seconds, achieving an average synchronization rate of 94.01% with the emotional content of the music. This approach not only proves effective for stage lighting design but also offers valuable insights for enhancing the emotional expressiveness of musical performances.

JEZIČKO-STILSKE DOMINANTE U ROMANU „NEBO, TAKO DUBOKO“

The paper explores particular stylistic features in the novel Nebo, tako duboko by Vesna Kapor, which are based on cumulating and separating lexical items. In addition, repetitions of identical or varied units and sentences, both contactual and distant, as one of the specific stylistic features of the novel are analysed as well. The most distinc- tive feature is the markedness of punctuation, especially commas, that deviates from the norm. Although the structure of the novel is polyphonic, from the point of view of linguo- stylistics the text is not differentiated. From the beginning to the end of the novel, cu- mulating and combining different stylistic features is prevailing, which results in a highly emotional language, with a broken rhythm, predominantly marked by pain and sadness

Classroom Quality Evaluation of English Teaching Activities Based on Probabilistic Language Information

A crucial component of kindergarten instruction, collective teaching activities are a good way to educate young children on their overall development. The language field is one of the subjects taught in kindergarten, and it has to do with how kids learn to read, write, and speak. In order to improve teachers’ comprehension of children’s emotional reactions and language, this paper combines quantitative and qualitative methods to observe and analyze the quality of current language collective teaching activities in kindergartens. It also suggests knowledge logic and psychological logic for grasping the content of language collective teaching in kindergartens. To improve the quality of language teaching in kindergartens, it is crucial to adopt a variety of teaching strategies and organizational techniques, provide the proper tools and materials for language learning, pay attention to the key experiences of children learning the language, and enhance learning quality.

Evaluating Emotional Language: A Comparative Appraisal Analysis of Urdu Poetry of Bhabha and Ajami

Evaluating Emotional Language: A Comparative Appraisal Analysis of Urdu Poetry of Bhabha and Ajami

Emotional Language Processing in Bilingualism: Subjective Affect and Prosodic Markers in Simultaneous Interpreting

Emotion, as part of the overall sensorimotor, introspective, and affective system, is an essential part of language comprehension within the framework of embodied semantics. As emotional state influences semantic and syntactic processing, emotional language processing has been shown to modulate mood as well. The reciprocal relationship between language and emotion has also been informative in bilingualism. Here we take a relatively underresearched type of bilingual processing, simultaneous interpreting, as a case of extreme bilingualism and investigate the effect of emotional language rendering in the L1 on subjective affect and prosodic markers of L2 output. 18 trainee interpreters were asked to simultaneously interpret three speeches in Turkish that varied in emotionality, valence (negative, neutral, and positive), and difficulty in English. Responses to emotional language processing were analysed based on participants’ self-reported positive and negative affect using the Positive and Negative Affect Schedule (PANAS) and three prosodic parameters (intensity, pitch, and fluency). Results showed that interpreting emotionally negative speech increased negative affect, whereas interpreting emotionally positive speech did not modify positive affect. Intensity generally reflected cognitive load. Pitch and fluency, in particular, were more sensitive to changes in the valence of the source speech.

Historical Development and Cross-Cultural Influence of Dance Creation: Evolution of Body Language

This paper delves into the historical evolution of dance creation and its cross-cultural influences, with a particular focus on the transformation of corporeal language. Commencing from the dance traditions of ancient cultures, the article retraces the dances of Ancient Egypt and Ancient Greece, as well as the religious and courtly dances of the medieval and Renaissance periods. Subsequently, it scrutinizes the emergence of modern dance, encompassing the American modern dance movement and European expressionist dance, along with the evolution of dance techniques and forms. The article delves into the varied manifestations of dance, including pure dance, narrative dance, and experimental dance compositions. It also analyzes the impact of cross-cultural influences and globalization on dance, encompassing dance's role as a medium for cultural dissemination, as well as the internationalization of dance education. Finally, by examining the evolution of corporeal language, it underscores the connection between dance and emotional expression and social corporeal language, including trends in dance creation and interdisciplinary research in the digital age. This article encapsulates the metamorphosis of dance as a form of corporeal language, emphasizing its significance and forthcoming challenges.

Co-creating The Learning and Teaching Journey with Postgraduate Research Students Who Teach

Post-graduate research students (PGRs) often have teaching roles during their studies with little formal training or support (Lueddeke, 1997). Previous support at Edinburgh Napier University consisted of voluntary, unpaid on-campus Learning and Teaching (L&T) training over three days (pre-pandemic). During lock-down, this course transitioned online to a voluntary 3.5-hour workshop and a follow-up Moodle course.
 To have a student-centred approach (Pedersen & Liu, 2003; Hannafin & Hannafin, 2010) and understand the needs and wants of PGRs who teach, we aimed to co-create with students (Cook-Sather, Bovill & Felten, 2014) effective PGR support and evaluate its impact. 
 Students were employed as partners (Matthews 2018; Dollinger et al. 2022) to assess support needs and explore options available for PGRs at 11 other UK Universities. Based on the assessment findings, a new programme was co-designed between the student-partners and L&T academics: three 1-hour online workshops and three corresponding peer-led discussion sessions. The student-partners also created a Microsoft Teams workspace for students to build community: sharing their own L&T experiences, practice, and engagement with relevant theories.
 These findings support our plans for upcoming mandatory and paid introductory teaching workshops, as a foundation for this longitudinal development of peer-relations and practice. This new structure and format has cultivated a community of practice constructed by students, for students (Wilson, Wilson & Witthaus, 2020). 
 An autoethnographic approach has been taken with the student interns and lecturers as both researchers and participants (Wall, 2008; Méndez, 2013; Cooper & Lilyea, 2022), by means of writing a narrative based on self-reflection on the process. This will be analysed by thematic coding (Gibbs, 2008) to discuss the experiences, thematic ideas, emotive language, and feelings expressed by students interns and lecturers in this co-creation journey.

The influence of emotional context on emotional word processing in discourse comprehension: evidence from event-related potential.

This research utilized event-related potential (ERP) recording technology to examine the effect of emotional context on the processing of emotional information in sentences. Three types of emotion-consistent discourse materials (neutral-neutral, positive-positive and negative-negative) were constructed to specifically express neutral, positive and negative emotions, respectively. Each discourse comprised two sentences, with the emotionally significant words embedded at the penultimate position of the second sentence. Participants were asked to read these texts, respond to reading comprehension questions and the ERP amplitude induced by the emotional words was recorded. The results indicated a tripartite interaction in the N400 and Late positive component amplitudes involving emotional context, emotional words and brain hemispheres, observed in both frontal and central brain regions. Notably, there was a significant difference in response to positive words between positive and negative contexts. The findings suggest that emotional context has a substantial effect on the processing of emotional words. Positive words, in comparison to negative ones, are more influenced by emotional context, particularly in the frontal and central regions of the brain.

Chinese readers utilize emotion information for word segmentation.

We reported a large-scale Internet-based experiment to investigate the impact of emotion information on Chinese word segmentation, in which participants completed an overlapping ambiguous string (OAS) segmentation task and the Chinese version of Beck Depression Inventory-II in a counterbalanced order. OAS is a three-character string (ABC) in which the middle character can form a distinct word with both the character on its left side (word AB) and the character on its right side (word BC). Participants were presented with isolated OASs and were asked to report the word they identified first. Emotional OAS was constructed by a combination of a neutral word and an emotional word, with the neutral and emotional words sharing character B. We orthogonally manipulated the valence of the emotional words (positive vs. negative) and their position in the OAS (left-side vs. right-side). The results showed that compared with neutral words, both positive and negative words were more likely to be segmented, and this segmentation outcome was not affected by readers with different depression tendencies. These findings suggest that emotion information can influence word segmentation, and that both positive and negative words take precedence over neutral words in the word segmentation process. This study provides a new perspective and evidence to understand the impact of emotion information on word processing.

POSITIVE EMOTION LANGUAGE PREDICTS IMPROVING DEPRESSION TRAJECTORIES AFTER DEMENTIA CAREGIVING ENDS

Abstract There are striking differences among caregivers of people with dementia (PWDs) in how their health and well-being changes during active caregiving; however, less is known about these differences after caregiving has ended. One important factor influencing caregiver health is the emotional quality of the caregiver-PWD relationship. The present study evaluated caregivers’ emotional connection to the PWD (during caregiving) and associations with caregivers’ depression both concurrently (N = 326 active caregivers) and longitudinally (n = 91 former caregivers). Active caregivers described a recent time they felt connected to the PWD, and their responses were transcribed verbatim. Caregivers’ emotional connection to the PWD was operationalized by summing the number of positive and negative emotion words from the transcripts using text analysis software and dividing each figure by the total number of words in the response. Results indicated that neither positive nor negative emotional language was significantly correlated with caregivers’ concurrent depression. However, two-wave latent change score modeling revealed that caregivers’ greater positive emotional language (adjusting for caregivers’ baseline depression and negative emotional language) predicted steeper declines in depression (i.e., improvements in depression trajectories) after the death of the PWD (ß = -.11, SE(ß) = 0.04, p = .033). Negative emotional language was not significantly associated with changes in caregivers’ depression. This finding highlights positive emotion expressed by caregivers when describing their connection with the PWD as a potential resilience factor and underscores the important role that positive emotional qualities of the caregiving relationship play in improving caregivers’ mental health after caregiving ends.