Emotional Tendencies Research Articles

An Optimized Public Opinion Communication System in Social Media Networks Based on K-means Cluster analysis

This study proposes a public opinion monitoring model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance the accuracy and effectiveness of public opinion monitoring on social media. The model’s performance across various dissemination indicators is studied in detail. Through experiments conducted on social media datasets, the study comprehensively evaluates the model from four dimensions: dissemination speed, scope, depth, and sentiment dissemination effectiveness. The experimental results indicate that the proposed optimization model excels in multiple areas, particularly in dissemination depth and sentiment dissemination effectiveness. Specifically, in the three dimensions of dissemination speed, the proposed model achieves scores of 4.3, 4.2, and 4.4 in initial dissemination speed, decay speed, and peak dissemination speed, respectively. In the dimensions of user coverage, geographic coverage, and platform coverage under dissemination scope, the model scores 4.4, 4.5, and 4.3, demonstrating a broad dissemination capability. Additionally, in the dimensions of hierarchical dissemination depth and key node influence within dissemination depth, the model scores 4.3 and 4.5, indicating excellent performance in multi-level dissemination and key node activation. In sentiment dissemination effectiveness, the model receives scores of 4.4, 4.5, and 4.4 in emotional tendency change, polarity distribution, and diffusion intensity, showcasing its advantages in sentiment classification and dissemination. Sensitivity analysis further validates the model’s sensitivity to parameter settings, with experiments showing that reasonable adjustments to parameters such as the K value, PSO inertia weight, and learning factors can reduce the Sum of Squared Errors to 3209.72. Meanwhile, it can improve clustering purity to 0.822 and raise the Rand index to 0.623. Therefore, this study offers an efficient and reliable solution for public opinion monitoring on social media, providing valuable reference significance.

Exploring the Role of Short Video Platforms in Guiding Public Opinion in the New Media Era

With the rapid development of short video platforms, they are gradually changing the way information is disseminated and public opinion is guided. To explore the role of short videos in guiding public opinion, this study mainly analyzes data from third-party data platforms and short video media accounts to reveal the mechanism of short videos on the evolution of public opinion. A detailed statistical analysis of the content of relevant videos on the short video platform was conducted, including the number of video views, the number of comments, and their changing trends; An in-depth analysis was conducted on the videos and comments released by self-media and mainstream media. Through content analysis, the study evaluated the communication characteristics of these media in guiding public opinion, including the selective dissemination of information, emotional tendencies, and the shaping of public cognition. The research results show that short video platforms play a significant role in guiding public opinion, and their dissemination characteristics and content selection have a profound impact on the formation and development of public opinion. In particular, self-media and mainstream media play a key role in guiding public opinion. They play a leading role in shaping public opinion through different communication strategies and content presentation.

The “Weird” Effect: Shaping U.S. Election Rhetoric

In 2024, as the U.S. election draws close, the media partition bias is increasingly obtrusive in its media framing. This essay explores the broader implications of this rhetorical shift by first analyzing the introduction of “weird” in the 2024 U.S. election discourse with Tim Walz’s viral comment and then comparing the term’s usage by MSNBC and Fox News to explore the partisan usage of the term and how they influence public perception, weaponize humor, and construct biased narratives. The comparisons will center around how semantic framing of words like “weird” creates effective negative priming for the audience and strengthens emotional tendencies such as cue-taking or confirmation bias. Finally, the essay discusses the broader implications of this rhetorical shift, embedding political battles into everyday life and complicating American political discourse. While not new in strategy, the essay underscores how this linguistic tactic uniquely infiltrates common language, reshaping political engagement in contemporary America and further polarizing American society.

Automated urban landscape design: an AI-driven model for emotion-based layout generation and appraisal

The evolution of a city is significantly shaped by the design of its urban landscape. The advancement of artificial intelligence has substantially increased convenience for individuals. This research proposes an urban landscape layout model powered by artificial intelligence that automatically generates urban landscape design based on deep learning (URDDL) with two dimensions: emotional tendency and urban landscape appraisal. The input image represents land use and surrounding road conditions, while the output image depicts the selection of the main entrance and the internal spatial function layout. The Pix2Pix model is trained to learn the internal function layout based on varying land use and road conditions. Additionally, a domain-specific dictionary is constructed using an existing semantic resource vocabulary, where positive and negative sentiment words are compared with their corresponding sentiment values, focusing on categories such as Stimulate, Sense, and Action. Experimental results indicate that the absolute average error of the URDDL model is 91.31%, with a maximum error of 96.87%. The degree of fit is highly appropriate for evaluating the emotional prediction of urban landscapes. The findings demonstrate that the URDDL model outperforms traditional design methods regarding generated results, suggesting its potential for future applications in automated landscape design.

Комментарий очерка Сергея Слонимского «Скандалист поневоле: хроника хронического стресса – 1972-1991» из книги «Заметки на полях нотной тетради»

The article continues the publication and commentary of S. M. Slonimsky’s 2002 essay “An Unwilling Scandalmonger: A Chronicle of Chronic Stress – 1972-1991” from the book “Notes in the Margin of a Music Score”. Due to space limitations, the entire material of the essay is divided into three parts: the first article is dedicated to the post-war period of the author’s life until the beginning of the 1970s. The second (this) article explores events from the composer’s life and work in 1972-1991. The following, third article will cover the last decade of the 20th century and the beginning of the 21st century, 1992-2002. The focal points of the author’s reflections in the second essay of the “chronicle of chronic stress – 1972-1991” are also themes of moral and ethical issues within the composer’s society. These themes are explored in a different light – in direct connection with S. M. Slonimsky’s life in chronological order. The essay covers the second half of the 20th century and the beginning of the 21st century. The writing of this commentary is driven by the intensifying crisis of moral values in contemporary society. The author concludes: one should “focus on only one painful moment, quite typical of my creative and life path. Perhaps scandalous storms are my cross, the fateful predetermination of my destiny. Perhaps it is a fatal necessity for an independent person walking their own path, not wanting to prudently submit to those in power, to what promises peace, recognition, success, prosperity, and happy success. That is how I am, and that is how I will remain until the end <…> In my work, my philosophical worldview, personal traits of my emotional temperament, and my attitude towards many people inhabiting Russia, Europe, Asia, our entire earth are absolutely obvious.” As a result of the study, Slonimsky’s unwavering commitment to the established moral and ethical order and his uncompromising stance against manifestations of evil, envy, and betrayal in the composers’ milieu and music teaching have been identified and confirmed.

Attitudes and preferences of the Chinese public towards products made from recycled materials: A text mining approach

Products made from recycled materials, as an environmentally friendly option, have attracted public attention. Understanding public attitudes and preferences towards such products is crucial for their market development. In order to explore the Chinese residents’ attitude and emotional tendency, we analyzed public discussions and topics on products made from recycled materials from Sina Weibo. Meanwhile, LDA and BERT deep learning models were employed to assess the level of public attention, hot topics, and sentiment tendencies in China. Results show a significant upward trend in public interest in products made from recycled materials. Although the fact that most people have a positive attitude towards the environmental concept of products made from recycled materials and support such products, there is not a significant demand for products made from recycled materials, i.e., there is a green attitude-behaviour gap. Results further reveal that factors such as the price of products made from recycled materials, branding, and public skepticism about the sustainability of the products are the main reasons for the gap between environmental awareness and consumer behaviour. By analyzing the public's cognitive structure and psychological characteristics regarding products made from recycled materials through social media data, this paper can help recycling manufacturers understand consumers' needs and expectations, as well as assist policymakers in formulating better circular economy policies.

The Impact of Cool and Warm Color Tones in Classrooms on the Perceived Emotions of Elementary School Students in Northwest China

Although it has been shown that color can influence mental health and behavior, few studies have discussed the effects of cool and warm colors in classrooms on the perceived emotions of elementary school students. In this study, we investigated the emotional changes of elementary school students in Yinchuan City, Northwest China in classrooms with cool and warm color tones. By using the Positive and Negative Affect Scale for Children (PANAS-C), the emotions of 123 third- to sixth-grade students in classrooms with cool and warm color tones were measured. We found the following conclusions: (1) Overall, the emotional responses of the subjects in both the cool- and warm-colored classrooms showed a tendency for positive emotions to be higher than negative emotions. (2) There was no significant difference between the effects of cool and warm colors on the overall emotion of elementary school students, but there were significant differences in specific emotions; Compared to warm colors, cool colors had a more significant effect on increasing feelings of calm (β = −0.365, p = 0.041). Compared to cool colors, warm colors were more likely to cause participants to feel mad (β = 0.186, p = 0.099). (3) The effects of cool and warm colors on students’ emotions differed significantly by gender and grade level. Cool and warm color tones had a significantly greater positive impact on females. In contrast, cool and warm colors had a more pronounced effect on males’ negative emotions. In addition, we found that grade level was significantly negatively correlated with overall emotion (β = −0.696, p < 0.001), with lower grades perceiving emotion more positively than higher grades. These findings provide important insights into the spatial design of elementary school classrooms and provide valuable comparative data for studies in different regional and cultural contexts, further enriching the empirical support of color psychology theory.

The effectiveness of applied behavior analysis program training on enhancing autistic children’s emotional-social skills

BackgroundThis study focuses on the potential of Applied Behavior Analysis (ABA) to improve emotional and social skills in children with autism spectrum disorder. ABA is a well-established therapeutic approach that uses behavior modification techniques to encourage positive behaviors and reduce challenging ones. Despite its widespread use, further research is needed to better understand its specific impact on emotional and social development in autistic children.ObjectiveThis research aims to investigate an effective method for improving and enhancing institutionalized children’s social, communicative, and daily life skills. The study also examines the impact of behavioral analysis on these children’s social and emotional skills.MethodThe research is categorized as applied in terms of objectives and quasi-experimental in data collection. It involves a control group, an experimental group, and a covariance analysis model. The research population consists of 100 volunteer boys aged 4 to 11 residing in institutional care in Wuhan during the year 2023. Among them, 60 individuals were selected and divided into control and experimental groups, each comprising 30 participants. Data for the study were collected using the kindergarten inventory of social/ emotional tendencies (KIST. The applied behavioral analysis program was implemented individually for the experimental group in eight one-hour sessions twice a week.FindingsData analysis was conducted using SPSS-24 software and a multivariate analysis of the covariance method. The results indicated that the behavioral analysis program significantly impacts institutionalized children’s social and communicative skills, improving their daily lives (p < .05).ConclusionThe findings of this study demonstrate that the applied behavior analysis program significantly improves the social, communicative, and daily life skills of institutionalized children with autism spectrum disorder. ABA interventions, delivered through structured sessions, effectively enhance emotional and social development, confirming its value as a therapeutic approach in institutional care settings.

Research on Optimization Path of Higher Education Management Based on Collaborative Filtering Algorithm

In order to better realize the optimization of university education management, this paper puts forward the research on the optimization path of university education management under collaborative filtering algorithm. The optimization of higher education management is divided into several management directions. First, the teaching resource recommendation management, using collaborative filtering algorithm based on emotional tendency, analyzes the comments of users of teaching resources, and recommends the teaching resources to users next time according to the emotional tendency obtained from the analysis. Second, the library book recommendation management, constructing the characteristic model of library book borrowing users, uses collaborative filtering algorithm to generate nearest neighbors, calculating the similarity of users, and recommending books that meet their characteristics for users according to the similarity. Finally, the management of personalized course selection recommendation in colleges and universities, which uses the historical information of students to establish the evaluation matrix of course selection, searches the nearest neighbors according to the evaluation matrix, and produces the personalized course selection recommendation results of the students according to the nearest neighbors. According to the experiment, the recommended users can be satisfied with the recommended results in different directions of education management. After applying this method, the overall quality of college students has been significantly improved, the expenditure of education management in colleges and universities has been significantly reduced, and the optimization effect of education management in colleges and universities has been remarkable.

Sonic emotion: variability of perception based on context and state

The study explored the momentary emotional disposition of 71 subjects (average age 23.2 years) by exposing them to six songs from different eras. Participants were asked to describe the emotions or feelings evoked by each song immediately after listening. This process was repeated after a 4-month interval, using the same songs in the same order to assess changes in emotional perception. Results indicated that in more than half of the cases, subjects provided different responses in the two stages of the study. This phenomenon suggests that emotional reactions to the respective songs were significantly influenced by the subjects' mood at the time of listening.

Tourist attraction reviews based on deep learning Sentiment Analysis System

In recent years, with the rapid development of information technology, online travel reviews have become a key source of information for tourists to understand destinations. In the face of massive and unstructured travel review data, this paper innovatively proposes a sentiment analysis system for tourist attraction reviews based on deep learning. The system cleverly uses the BERT pre-trained model, and combines Bi-LSTM and attention mechanism to significantly improve the accuracy of sentiment classification. In order to verify its effectiveness, this paper conducts experiments on the evaluation data of scenic spots in Hubei Province. The experimental results are very impressive through multi-dimensional indicators such as accuracy, precision, recall, and F1-score. The accuracy rate is as high as 93.5%, the accuracy rate is 94.1% for positive reviews, the accuracy rate for negative reviews is 92.8%, the recall rate is 92.1%, and the F1-score is 92.8%. This fully shows that the system can accurately identify the emotional tendency of tourist attraction reviews, provide a strong basis for scenic spot management, help it adjust service strategies in a timely manner, and also assist tourists to make more informed decisions, providing a reliable technical reference for the digital transformation and intelligent management of the tourism industry.

What is the role of affective forecasting in knowing what we value?

ABSTRACT Generally, we confidently ascribe valuing states to ourselves. We make statements such as “I value democracy” or “I value my best friend” - our sense of who we are depends on doing so. Yet what justifies that confidence? If you were asked “Do you value philosophy, or are you just doing it for the money?”, how might you go about generating such knowledge? I will operate with the notion that valuing involves, at a minimum, a set of distinctive emotional dispositions toward the valued object. Given this view of valuing, a plausible explanation stemming from Tooming and Miyazono is the following: to know whether we value, we need to predict our potential or future emotional reactions. That is, they suggest that affective forecasting is necessary to generate knowledge of valuing states. I argue that, despite its intuitive appeal, affective forecasting is not necessary. I then consider a more modest claim, namely that affective forecasting is still explanatorily powerful when it comes to self-knowledge of valuing states, as it explains the difficulty of generating such knowledge. I reject this further claim, arguing that there is a stronger available explanation: the use of theories about valuing.

An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets.

The Internet's development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public's demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian-Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model's hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters.

Assessing Affective Dimensions of the Interpersonal Circumplex: Development and Validation of the Interpersonal Emotion Inventory

Interpersonal emotions are feelings distinctively associated with real or imagined interpersonal situations. We propose that interpersonal emotions can be organized within the same circumplex used by contemporary integrative interpersonal theory to organize other aspects of interpersonal functioning. Our research explores the affective dimensions underpinning the interpersonal circumplex (IPC) and maps distinct emotional dispositions using coordinates for agentic and communal affect. Our aim is to provide a better differentiated and more comprehensive taxonomy of interpersonal emotions consistent with the IPC framework. In an initial development study (N = 1223 undergraduates) we used circumplex and psychometric criteria to create a 64-item Interpersonal Emotion Inventory (IEI), a novel self-report measure of interpersonal emotions reflecting each IPC octant. We then tested the structural properties and utility of the IEI in a preregistered validation study (N = 278 community participants). Across both studies, the IEI scales exhibited good fit to a two-dimensional circular model. The IEI demonstrated robust convergent, discriminant, and incremental validity in relation to measures of interpersonal values and problems, positive and negative affect, adult attachment, self-conscious emotions, self-esteem, and facets of narcissism. By complementing existing measures of affect and interpersonal phenomena, the IEI should prove useful in both research and clinical contexts.

Pupillary Responses to Dynamic Negative Versus Positive Facial Expressions of Emotion in Children and Parents: Links to Depression and Anxiety.

Witnessing emotional expressions in others triggers physiological arousal in humans. The current study focused on pupil responses to emotional expressions in a community sample as a physiologicalindex of arousal and attention. We explored the associations between parents' and offspring's responses to dynamic facial expressions of emotion, as well as the links between pupil responses and anxiety/depression. Children (N=90, MAge=10.13, range=7.21-12.94, 47 girls) participated in this lab study with one of their parents (47 mothers). Pupil responses were assessed in a computer task with dynamic happy, angry, fearful, and sad expressions, while participants verbally labeled the emotion displayed on the screen as quickly as possible. Parents and children reported anxiety and depression symptoms in questionnaires. Both parents and children showed stronger pupillary responses to negative versus positive expressions, and children's responses were overall stronger than those of parents. We also found links between the pupil responses of parents and children to negative, especially to angry faces. Child pupil responses were related to their own and their parents' anxiety levels and to their parents' (but not their own) depression. We conclude that child pupils are sensitive to individual differences in parents' pupils and emotional dispositions in community samples.

Influence of Positive/Negative Emotions Towards Autobiographical Memories on Impressions of the Conversation and Conversation Partner During Nostalgic Memory Sharing

ABSTRACTWe investigated the effects of positive/negative emotions towards autobiographical memories on impressions of the conversation and conversation partner during memory sharing in a reminiscence therapy setting. Fifty‐six older (Mage = 69.3) and 60 younger (Mage = 20.7) participants' net emotional tendency (positive/negative) towards autobiographical memories and depressive symptoms were evaluated using validated rating scales. Positive/positive and positive/negative age‐matched random pairs shared a nostalgic memory through free conversation. Participants with a negative emotional tendency towards autobiographical memories in positive/negative pairs tended to feel less “easy” about the conversation. Participants with a positive emotional tendency towards autobiographical memories in positive/negative pairs had a more negative impression of their partners. Our data provide insight into the relationship between net emotional tendency towards autobiographical memories and depression and suggest that an individual's net emotional tendency towards autobiographical memories potentially affects the outcome of group reminiscence therapy.

Exploring the Manifestations, Influencing Factors and Attitudes of Depression in Generation Z in Chengdu

In this period of social transformation, as Generation Z faces a more complex macro-environment, they are exhibiting contradictory and negative emotional tendencies that can lead to depression. Through an investigation and analysis conducted in Chengdu, China, the study found that Generation Z youth primarily exhibit depressive mood, cognitive retardation, sleep disorders, loss of interest, fatigue, and other psychological or spiritual aspects as manifestations of depression. The key factors influencing depression in Generation Z include social pressure, family relationships, physical reasons, and sudden disasters. In terms of attitudes, Generation Z youth generally display a more positive outlook when experiencing depression, but they tend not to prefer psychologists or counselors as a solution.

Personalized movie recommendations based on probabilistic linguistic sentiment and integrated DEMATEL-TODIM methods

With the development of society, online reviews are increasingly becoming a crucial factor in decision-making. Especially for entertainment products such as movies, they are preferred for their affordability and high entertainment factor. Therefore, this paper proposes a movie recommendation model that considers user personalization using a probabilistic linguistic approach based on online reviews. Firstly, the method constructs a quantitative sentiment framework that transforms comments into a multi-granular probabilistic sentiment language. Secondly, we build the decision-making trial and evaluation laboratory (DEMATEL) method for probabilistic linguistic environments to explore interrelationships between product attributes, and improve the distance measure and score function to better integrate probabilistic linguistic information into DEMATEL weight calculations. Furthermore, to account for risk preferences, the model employs the extended TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to determine the ranking of alternatives. Finally, we design Douban movie ranking experiments to demonstrate the validity of the model. Compared with other methods, this paper incorporates the emotional tendency of movie attributes and user preference into the decision-making process leading to more reasonable results.

Transgressive play or playful transgressions? Dynamic interpretations of toxic behaviour in multiplayer online games

ABSTRACT Toxic behaviour in the gaming sphere is variably and inconsistently defined even though it is frequently used in gaming-related literature. It also tends to be used interchangeably with tangential terms (e.g., trolling, griefing) that aren’t conceptually equivalent. Thus, although toxic behaviour has been flagged as a prevalent and problematic issue in online gaming, there remain challenges for effectively assessing its impact on gaming experience and player well-being due to definitional uncertainties. The present study is an attempt to reconcile these uncertainties through an exploration of how players define and recognize toxic behaviour in multiplayer online games. Through semi-structured interviews with 30 online gamers, we propose that toxic behaviour in MOGs can be defined by highlighting the socially undesirable nature of an action, its detrimental effects on victims, and the perpetrator’s intentionality in executing an action. Interestingly, there is a paradoxical disconnect between how players conceptually define toxic behaviour, and how they practically recognize it in actual gameplay. The latter can often be impulsive, or otherwise involve a dynamic evaluation influenced by their attitudinal and emotional dispositions, the people that they are playing with, and the gameplay setting. These interpretations sometimes deviate from the understood definition in a decontextualized setting. Findings from this study have strengthened our understanding of how players dynamically navigate social encounters in games, and how they distinguish between toxic and benign gameplay interactions. An emergent typology of toxic behaviour is also proposed along with some recommendations for future research directions.

Profiling Parent’s Responses to Children’s Anxiety: A Qualitative Study Combined with Multiple Correspondence and Cluster Analyses

Parental influence and children’s anxiety have a complex interaction. Robust findings revealed that parenting styles and practices, modeling, and parent-child emotion socialization play a role in a child’s anxiety. However, research has focused on global behavioral and emotional tendencies derived from quantitative studies with large samples, neglecting their link to parental cognitive factors and the heterogeneity of the family’s distinctive experiences. Our study aimed to broaden this knowledge, capture what parents think, feel, and behave during anxiety-enhancing childhood experiences, and identify distinct parental profiles based on these complementary elements. The present study adopted a cross-sectional qualitative design. Semi-structured interviews were conducted with 65 parents of children with anxiety disorders (9–12 years old). A content analysis was first performed, and then representations of the associations between the emergent categories obtained from the content analysis, and latent constructs that can work as major determinants in parents’ responses to the child’s anxiety, were assessed by a multiple correspondence analysis (MCA) combined with a hierarchical clustering algorithm. Parental cognitions, emotions, and behaviors are first derived into two distinct dimensions: capacity to respond and emotional reactivity. Three typological profiles of parents were derived from these dimensions: (a) Reactive and ineffective; (b) Unreactive and moderately effective, and (c) Anxious and effective. The qualitative exploration of parental cognitions, emotions, and behaviors in a child’s anxiety context contributes to engrossing the current literature. Our results have essential implications and the need to adjust treatments in clinical settings according to parents’ profiles.