This study aims to introduce an integrated model for understanding the influence of various sentimental factors in conjunction with macroeconomic factors on portfolio returns across ten industry sectors within the US market. These sentimental factors are categorized into market-wide, consumer, and individual stock market factors to assess their impact on industry portfolio returns. Employing the Autoregressive Distributed Lag (ARDL) model, the study evaluates the effects of macroeconomic and sentimental factors on stock market portfolio returns. The findings reveal a negative relationship between short-term interest rates and portfolio returns in specific industry sectors like manufacturing, telecom, and wholesale/retail. The study finds a positive relationship between the Hi-tech sector's risk spread and portfolio returns. Market sentimental factors positively influence portfolio returns of durable, non-durable, utility, and other sectors. Individual sentimental factors negatively impact portfolio returns in hi-tech, utility, durable, energy, and other sectors. The stock market-related individual, sentimental factor of the number of IPOs has a positive impact on portfolio returns in the energy sector and a negative impact on portfolio returns in other sectors. Consumer sentimental factors are significant positive determinants for portfolio returns in durable, energy, telecom, health, and other sectors. Discounts on closed-end funds may provide vital fundamental information regarding lower future earnings for stocks in the durable and energy sectors. The study provides valuable insights for investors to optimize their portfolio strategies in response to macroeconomic and sentimental factors within specific industry sectors.
With the rapid advancement of the Internet, emerging social media platforms facilitate real-time interaction among users, thereby rendering the impact of sentiments on behavior both faster and more complex. Analyzing and predicting the influence of sentiments on behavioral changes under various factors has become a critical issue. Grounded in the Emotions as Social Information (EASI) theory, this study conducts a comprehensive dynamic analysis of user sentimental changes from both personal and interpersonal perspectives. We employ HanLP for sentiment analysis and utilize structural equation modeling (SEM) and chi-square tests to analyze and validate the impact of sentiments on behavior. The results indicate that positive personal sentiment changes in users significantly enhance their purchase intentions. Furthermore, different users exhibit varying sentimental changes in their self-imitation behaviors. While positive emotions significantly influence users' repetitive posting behavior, the effect of repeated video watching is less pronounced. This study, incorporating both real-time and video-time dimensions, dynamically validates that users who imitate others' behaviors display more consistent positive emotions, providing evidence for sentimental contagion in user behaviors.
Warning messages, such as "In case of emergencies such as floods, high water, or landslides caused by persis-tent meteorological conditions in our country, please call the emergency call center" are commonly used in disaster management. To effectively manage a disaster and develop appropriate strategies, it is crucial to ensure a two-way flow of information. With the advent of social media, this two-way interaction has expanded significantly, enabling large-scale engagement through these platforms. This study aims to analyze the public's social media response to the first-ever experiment with an audio warning system for severe weather. The primary objective is to assess the public's reaction to technological innovation in the field of disaster management. The findings from this study can be utilized to enhance disaster education within society. Furthermore, the study's methodology will serve as an essential tool for de-cision-makers involved in early warning systems, facilitating a smooth transition to new technologies. Additionally, this study presents a detailed description of the language processing procedure employing a multilabel natural language processing model. The model specifically focuses on analyzing social media comments, which are considered unclean text within the context of this study.
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Twitter data analysis gives valuable insights into various aspects of society, such as consumer opinions, political sentiments, brand reputation, and more. This information can help businesses and organizations make informed decisions, track the success of marketing campaigns, and identify emerging trends. Additionally, Twitter data analysis can also aid in research fields such as social sciences and humanities by providing a large, real-time dataset of human behaviour and language. Sentiment Analysis uses natural language processing and machine learning algorithms to categorize tweets as positive, negative, or neutral based on the sentiment expressed in the text. The previous sentiment analysis literature has cited several drawbacks especially on frequency-based vectorization models like Bag of Words, TF-IDF, and traditional word embeddings that generally cannot capture semantic relationships and contextual dependencies in short and noisy Twitter data. The proposed work comprises two phases. In the first phase, text pre-processing, vectorization, word embedding and feature selection is performed using the Frequency Co-occurrence Matrix and Fisher's score algorithm. In the second phase, the Multi stacked BiLSTM is implemented to perform classification as positive, negative and neutral. The performance of the proposed work achieves an accuracy and mean squared error rate as 98% and 0.01% respectively.
The Geneva Sentimentality Scale (GSS) measures the experience of being moved and its effects on behavior. Despite the prevalence of this emotional response, it has not been extensively studied in China. This study aims to adapt and revise the GSS for Chinese college students to assess its cross-cultural consistency. A sample of 1328 students aged 18-24 years participated in the study, with 127 randomly selected for retesting after an 8-week interval. Exploratory factor analysis reveals that the Chinese version of the GSS includes three factors (emotional labels, tears of joy, and warm feelings in the chest), with a total of nine items. The internal consistency coefficients for the three factors and the overall scale are high, and the total score remains stable over time. Confirmatory factor analysis (CFA) shows that the three-factor model has a good fit. Multigroup CFA indicates measurement invariance across genders. The results also demonstrate good discriminant and convergent validity for the scale. Overall, the GSS is a reliable and flexible tool for assessing the emotion of being moved among Chinese college students.
Astrology, magic, and other psychic healing practices are undergoing a cultural revival, notably among those on the Left who employ it as a language for social justice. Queer practitioners have claimed kinship with the occult through a perceived shared abjection, deeming it an inherently queer resource for self- and community empowerment, and naming anti-racism and decolonization key aims of their work. At the same time, these forms of occultism draw suspicion, not least among practitioners themselves, who are critical of the ways these knowledge traditions have been complicit in 'spiritual genocide'. Drawing from ethnographic fieldwork with 30 informants in Montréal in 2022, I investigate the occult's appeal among queer people as a process of affective expansion, wherein practitioners attune to heretofore repressed lifeways, knowledges and worlds that machineries of empire have rendered invisible. If contemporary occult movements represent a turning towards putatively repressed modalities that may rival those which we have otherwise inherited, queer informants claim a special relationship to these objects through a framework of sensitivity that magic allows them to workshop. Theorizing the occult as a biopolitical affect regime, I argue that informants ironically invest in historically racialized language of impressibility as indexes of social health at the same time that they locate queerness, rather than whiteness, as a conduit for that affective expansion. I argue that white informants demonstrate a particular anxiety about how to learn to become open to this otherwise, positing 'bottoming' as a spiritual and political imperative to become receptive to forms of accountability, reparations and solidarity. How does the occult represent an attempt to build capacity for receptivity among participants, and how do they link this capacity to the healing of white supremacy and decolonization?
Choosing the appropriate career path poses a significant hurdle for students, especially when time is constrained. This research addresses the challenge of career prediction by introducing a method that integrates additional attributes, refines feature prioritization, and streamlines feature selection to enhance prediction precision. The key objectives of this study are to pinpoint pertinent features, accurately rank them, and enhance prediction accuracy by eliminating non-essential features. To accomplish these aims, three methodologies are employed: Feature Fusion and Normalization (FFN) for precise data identification, Average Feature Ranking (AFR) utilizing a blend of Random Forest (RF) and Linear Regression (LR) for feature prioritization, and Improved Prediction with Weighted Characteristics (PWF) which integrates Principal Component (PC) analysis for feature reduction. The prediction performance is assessed using a hybrid Multilayer Perceptron (MLP) classifier with 5-fold cross-validation. The outcomes reveal that the hybrid approach yields a superior feature set for prediction. The top twelve ranked features are determined by averaging each feature's RF scores and coefficients. The achieved accuracy (ACC), precision (P), recall (R), and F1 scores stand at 87%, 87%, 86%, and 86%, respectively, with an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) value of 92%. These findings underscore the efficacy of the proposed hybrid learning technique in accurately forecasting career trajectories.
Recently, a documented increase has been observed in fake news and broadcast of such reports leads to grave danger to individual as well as societal welfare. There's a danger of political collapse and a subsequent devastating loss of public confidence. The overwhelming quantity of news spread online leads towards impractical manual verification and due to the subtle distinctions within language, detecting fake news is an arduous challenge due to the ability to produce coherent and significant. Nowadays advanced neural language models (NLMs) are frequently utilised widespread in sequence generation domains. Additionally, they may be used to create false reviews, which can subsequently be used to target online review platforms and sway consumers' purchasing choices. This research explores the application of blockchain technology and sentiment analysis to create a privacy-focused system for detecting and analyzing fake web recommendations. The input data comprises sentiment-based features extracted from web recommendations. A generative convolutional Bernoulli bayes neural network is employed for the feature extraction and classification. Further, to strengthen network privacy, blockchain technology has been integrated with federated learning. This work offers an experimental analysis of diverse sentiment data-driven fake recommendation datasets, evaluating performance using accuracy, precision, recall, and F-measure metrics. A comprehensive evaluation of effectiveness is performed for each classifier. Results from the classification process indicated that a predictive model could be developed, leveraging tweet data, to distinguish between spam and non-spam content and to determine associated sentiment. The proposed method achieved 99% accuracy, 94% precision, 93% area under the curve, 94% recall, and 96% F-measure.
Santiago Ramón y Cajal (1852-1934) revolutionized the branches of neuroscience in a forceful way, and he did it with extreme delicacy and candor. His scientific writings and drawings are full of allusions to Nature, a fact that demonstrates how he saw, understood and enjoyed it with exquisite sensitivity and pressing emotion. Neuroscience awakened in him the utmost curiosity to delve into the powerful mysteries of the mind, and neurohistology allowed him to satisfy his deepest concerns for fascinating scenarios, a desire not sufficiently fulfilled throughout the fields, mountains and forests of his childhood and youth. Through that wonderful microscopic world Cajal changed the size of the dreamed landscapes but not the dimension of the longed-for adventures. Exploring and entering unknown paths he unraveled some of the greatest enigmas that the nervous system hid, but he would do so with a deep feeling toward the infinite beauty that Nature itself offered him. In short, Nature was the vital axis of Cajal's overwhelming and complex personality, his most genuine essence and the inexhaustible source of inspiration where he poured his imagination and fantasy. He became a vocational adventurer, an insatiable explorer, a talented artist and an exquisite humanist. An eminently romantic soul who knew how to link Nature and Neuroscience with unconditional and perpetual emotionality.
To examine whether deficits in social cognition (SC) are associated with poorer schizophrenia-related quality of life (SQoL) through psychological distress (PD) in adults with schizophrenia spectrum disorders, and to outline implications for community mental health and recovery-oriented care. We studied 175 outpatients (58% male; M age = 41.1) with schizophrenia spectrum disorders. Patients completed the GEOPTE Social Cognition subscale, the Kessler Psychological Distress Scale (K10) and the Schizophrenia Quality of Life Questionnaire (S-QoL-18). A structural equation model (SEM) with weighted least squares mean and variance adjusted estimation (WLSMV) tested direct and indirect effects (SC → PD → SQoL) across five domains. Greater SC impairment correlated with higher PD (ρ = .48, p < .01) and with lower psychological/physical well-being, friendships and sentimental life (ρ = -.46 to -.24). Model fit was excellent (χ2/df = 1.50; RMSEA = 0.05, 90% CI [0.044, 0.064]; CFI/TLI = 0.96; SRMR = 0.06). In the SEM, SC showed strong direct effects on psychological well-being (β = -.77, p < .01) and moderate effects on physical well-being (β = -.34, p = .03), while indirect-only mediation via PD emerged for friendships and sentimental life (β_ind = -.14, p = .01; β_ind = -.25, p < .01). SC deficits are closely linked to both intrapersonal and interpersonal aspects of SQoL. These pathways support integrating SC assessment/remediation and PD management within community-based, recovery-oriented services, with particular attention to interpersonal domains of quality of life.
This article examines Charles Bell's experimental practices by drawing historiographical attention away from the priority disputes over the spinal nerve functions for which he was most famous. I argue that Bell's primary research interest was the expression of emotions. To this end, he developed a programme of vivisection that explored the underlying mechanisms of emotion. However, this also resulted in a profound contradiction between his experimental practices and his worldview - conducting painful experiments on beloved animals despite moral revulsion towards animal experimentation. This opens up three interconnected areas. Firstly, it allows an exploration of disciplinary identity in medicine, particularly the way that disciplines demanded specific practices and behaviours. Secondly, vivisection more generally required methods and ethics that opposed the growing anti-cruelty voice. Here, a combination of animal choice and the importation of techniques from the slaughterhouses was critical. Thirdly, vivisectors navigated a complex emotional landscape between their professional obligations and broader cultural sensibilities. These three areas are linked together using Boddice's concept of moral economies, the affective frameworks that structured feelings. Particularly important were the sentimental and Romantic economies, both of which impacted Bell and his research. At the same time, Bell always struggled to reconcile the tensions between his disciplinary identity and his sentimental and Romantic beliefs, ultimately leading him to abandon experimentation after his assistant John Shaw's death. I conclude by identifying the guarantees provided by character for licensing ostensibly cruel behaviours, thus allowing for the maintenance of probity within competing moral economies.
In this article, I investigate the trade dynamics of Bitis, a genus of African vipers, within the exotic pet market, with a particular focus on trade flows between South Africa and Europe. The conservation status of the 18 recognised Bitis species ranges from Least Concern to Endangered, with official assessments primarily attributing threats to environmental destruction. Employing Nicolini's "zooming-in, zooming-out" method, I trace the practice of trading Bitis through netnography, in-depth interviews, and field visits to examine the socio-ecological dimensions of this semi-regulated trade. This article presents the findings of this multi-sited research. I outline the mechanics of the trade, which, despite being largely legal, often involves grey and illegal activities due to regulatory ambiguity and inconsistency. The underlying value structures driving the trade include the ornamental appeal of Bitis, along with secondary motivations such as its behavioural traits, sentimental value, and perceived rarity. I show how these value-making practices co-produce the socio-ecological conditions under which Bitis are collected, bred, and traded, and how these conditions can undermine or enable attempts to regulate the trade.
This paper revisits the longstanding debate over the nature of suffering, focusing on the divide between subjective and objective accounts. I defend a Personalist conception of suffering, rooted in an Aristotelian understanding of human flourishing, that recognizes suffering as both universally human and deeply personal. On this view, suffering is neither a purely sentient, inner experience nor reducible to external conditions, but a disruption of flourishing that arises when love or justice is violated or absent-and that calls for a communal response. Understood through this lens, suffering, I argue, invites a shared practice of meaning-making-not as sentimental optimism but as a form of grounded hope: realistic, responsive, and attuned to the dignity of both the sufferer and those who accompany them. Even when suffering cannot be cured or fully comprehended, it can be met with deeper engagement, mutual responsibility, and a reaffirmation of our commitment to a life lived in relation and shared purpose.
Smartphone use among older adults has become increasingly important, shaping social inclusion and daily life. This study examined public discussions on X (formerly Twitter) regarding smartphone use in older adults, comparing Persian-speaking and English-language communities. Tweets in English and Persian were collected from June 1 to June 30, 2024, and analyzed using Braun and Clarke's thematic analysis approach. Six themes emerged in the English-language dataset, including digital exclusion, learning and adaptation, usability challenges, preference for simplicity, vulnerability to digital risks, and intergenerational support. Persian tweets revealed five main themes, overlapping with those identified in the English-language data, but highlighting stronger emotional and cultural dimensions, such as sentimental value in digital interactions. Overall, discussions reflected experiences ranging from empowerment to exclusion, underscoring the importance of inclusive technology design, culturally sensitive digital literacy programs, and policies addressing accessibility barriers for older users.
High-quality user experience improvements across e-commerce or content platforms are inherent in recommender systems. Traditional recommendation approaches, such as collaborative filtering (CF) and content-based filtering (CBF), are typically grounded only in user-item interaction data and often neglect the valuable semantic and sentimental knowledge embedded in user review data. Unfortunately, that limits how personalised and engaging it can be. In recent years, sentiment-aware recommendation has seen significant development. However, these models essentially combine sentiment information in shallow, static representations that fail to support adaptive personalisation and capture deep semantics. To overcome these limitations, in this paper, we propose a deep learning based sentiment-aware product recommendation system, namely DeepSentRec, that tightly integrates sentiment analysis, hybrid filtering and reinforcement learning. SentimentBERT is employed to obtain fine-grained sentiments from review texts, and HybridCF-SBERT combines collaborative and semantic content-based similarities. RLRanker-PPO, a reinforcement learning-based component, adaptively ranks recommended items based on user engagement behaviours. Such a three-step architecture enables strong, dynamic, and personalised product recommendations. The proposed system has been evaluated on four publicly available datasets: Amazon Reviews, Yelp Dataset, IMDB Sentiment Dataset, and Kaggle E-Commerce Reviews. Our method achieves 91.12% precision, 85.49% recall, 89.66% NDCG, and 24.96% CTR, significantly outperforming SOTA baselines. Experimental results demonstrate that DeepSentRec improves recommendation accuracy and increases user satisfaction and activity. Highly Modular & Adaptive in Nature — Diverse & high potential to accelerate and benefit from end-use across diverse domains, essentially a way of being a next-generation intelligent recommendation framework.
As healthcare accelerates into an era defined by artificial intelligence, precision medicine, and advanced technologies, nursing leadership faces a critical inflection point. This article argues that the most essential leadership capability for nursing now and in the decade ahead is heart-centred leadership: the intentional integration of compassion, ethical integrity, relational awareness and strategic competence. Far from being sentimental, heart-centred leadership is positioned as a practical and evidence-based response to escalating burnout, moral distress, and the erosion of nursing's professional identity-challenges intensified by the COVID-19 pandemic and ongoing workforce shortages. Drawing on nursing theory and contemporary leadership research, the article demonstrates how leadership grounded in psychological safety, moral resilience and authentic human connection strengthens patient outcomes, supports workforce sustainability and enables learning in complex healthcare systems. It highlights how heart-centred leaders approach error, technology adoption and performance measurement in ways that protect the nurse-patient relationship while maintaining organisational accountability. Attention is also given to the role of women leaders, equity and social justice and the necessity of leader wellbeing as a foundation for compassionate cultures. The article concludes that as healthcare becomes increasingly technologically sophisticated, nursing leadership must become more intentionally human-centred. Leading with heart ensures that innovation serves healing rather than eclipsing it, safeguarding nursing's core purpose of alleviating suffering and promoting human flourishing.
Nostalgia is a self-relevant, socially grounded emotion marked by a sentimental longing for the past. Compared to the well-documented benefits known about general personal nostalgia, most nostalgia studies in both emotion and relationship science rely heavily on Western samples, and nostalgic effects of former partners are mixed. With an Asian sample, the present research had two main aims: (a) Experimentally replicate the nostalgic effects of current and ex-partners on current relationship quality, and (b) examine the moderating role of relationship duration. Results revealed both ex- and romantic nostalgia increased relationship satisfaction, and, more interestingly, romantic nostalgia decreased relationship commitment in shorter relationships. Furthermore, exploratory analyses highlighted distinct affective patterns within romantic nostalgia. Implications and contributions are further discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Kaathal- The Core is a Malayalam courtroom drama directed by Jeo Baby, which was released in November 2023. The film discusses how forced heterosexual relationships negatively impact not only homosexual individuals but also their heterosexual spouses, especially in a conservative community. The film revolves around Mathew, a retired bank manager and a leftist candidate in a Gram Panchayat by-election, whose candidacy is jeopardized when his wife Omana files for divorce, citing his homosexuality and failure to fulfill marital duties. As Mathew struggles to conceal his sexuality, he faces both ridicule and support from various factions. The plot gradually reveals how Devassy, his father, forced him to marry a woman despite knowing that his son was gay. The film takes a significant narrative shift when Devassy overcomes his dogmatic beliefs, supports Mathew's coming out, and facilitates Omana's pursuit of legal separation. The film challenges embedded heteronormative social norms while leaning into sentimental storytelling, making it both a bold statement and an emotional drama. It criticizes the stark enforcement of heterosexuality, marital oppression, and societal hypocrisy. The director strategically casts Mammootty and Jyothika, two highly acclaimed South Indian actors with immense stardom and widespread appeal, to broaden the film's reach and impact.