Search Results (244)

Sentiment analysis plays a crucial role in domains such as financial news, user reviews, and public opinion monitoring, yet existing approaches face challenges when dealing with long and domain-specific texts due to semantic dilution, insufficient context modeling, and dispersed emotional signals. To address these issues, a multi-granularity attention-based sentiment analysis model built on a transformer backbone is proposed. The framework integrates sentence-level and document-level hierarchical modeling, a different-dimensional embedding strategy, and a cross-granularity contrastive fusion mechanism, thereby achieving unified representation and dynamic alignment of local and global emotional features. Static word embeddings combined with dynamic contextual embeddings enhance both semantic stability and context sensitivity, while the cross-granularity fusion module alleviates sparsity and dispersion of emotional cues in long texts, improving robustness and discriminability. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed model. On the Financial Forum Reviews dataset, it achieves an accuracy of 0.932, precision of 0.928, recall of 0.925, F1-score of 0.926, and AUC of 0.951, surpassing state-of-the-art baselines such as BERT and RoBERTa. On the Financial Product User Reviews dataset, the model obtains an accuracy of 0.902, precision of 0.898, recall of 0.894, and AUC of 0.921, showing significant improvements for short-text sentiment tasks. On the Financial News dataset, it achieves an accuracy of 0.874, precision of 0.869, recall of 0.864, and AUC of 0.895, highlighting its strong adaptability to professional and domain-specific texts. Ablation studies further confirm that the multi-granularity transformer structure, the different-dimensional embedding strategy, and the cross-granularity fusion module each contribute critically to overall performance improvements. Full article

We investigate a variation of the classical voter model where the set of influencing agents depends on an individual’s current opinion. The initial population is made up of a random sample of equally sized sub-populations for each state, and two types of interactions are considered: (i) direct neighbors and (ii) second neighbors (friends of direct neighbors, excluding the direct neighbors themselves). The neighborhood size, reflecting regular network connectivity, remains constant across all agents. Our findings show that varying the interaction range introduces asymmetries that affect the probability of consensus and convergence time. At low connectivity, direct neighbor interactions dominate, leading to consensus. As connectivity increases, the probability of either state reaching consensus becomes equal, reflecting symmetric dynamics. This asymmetric effect on the probability of consensus is shown to be independent of network topology in small-world and scale-free networks. Asymmetry also influences convergence time: while symmetric cases display decreasing times with increased connectivity, asymmetric cases show an almost linear increase. Unlike the probability of reaching consensus, the impact of asymmetry on convergence time depends on the network topology. The introduction of stubborn agents further magnifies these effects, especially when they favor the less dominant state, significantly lengthening the time to consensus. We conclude by discussing the implications of these findings for decision-making processes and political campaigns in human populations. Full article

The increasing availability of User-Generated Content during large-scale events is transforming spectators into active co-creators of live narratives while simultaneously introducing challenges in managing heterogeneous sources, ensuring content quality, and orchestrating distributed infrastructures. A trial was conducted to evaluate automated orchestration, media enrichment, and real-time quality assessment in a live sporting scenario. A key innovation of this work is the use of a cloud-native architecture based on Kubernetes, enabling dynamic and scalable integration of smartphone streams and remote production tools into a unified workflow. The system also included advanced cognitive services, such as a Video Quality Probe for estimating perceived visual quality and an AI Engine based on YOLO models for detection and recognition of runners and bib numbers. Together, these components enable a fully automated workflow for live production, combining real-time analysis and quality monitoring, capabilities that previously required manual or offline processing. The results demonstrated consistently high Mean Opinion Score (MOS) values above 3 72.92% of the time, confirming acceptable perceived quality under real network conditions, while the AI Engine achieved strong performance with a Precision of 93.6% and Recall of 80.4%. Full article

What determines whether the public favors anthropocentric or biocentric signage in urban contexts? We conceptualize signage not only as a communicative device but also as a governance instrument that encodes environmental values into urban spaces. We study a city-level case of human–wildlife coexistence involving wild boars in Mount Carmel and Nesher (Israel) using a public opinion survey of residents (N = 405) and an operationalization that combines open-ended coding of the proposed sign content with structured items on sign design preferences. Analyses (correlations and regression models with mediation and moderation tests) indicate that higher perceived harm is associated with stronger anthropocentric preferences; this relationship is partly transmitted via support for local environmental morality policies and is conditioned by political ideology. These findings collectively show that socio-cultural stability, perceived harm, and political worldview jointly shape whether residents endorse signage that emphasizes human safety or ecological coexistence. Design choices also align with the spectrum: biocentric preferences co-occur with instructional/informational content, softer color palettes, family-oriented iconography, and humorous tones. By empirically operationalizing signage preference and linking it to socio-cultural and political drivers, this study clarifies how “design governance” can shape human–wildlife interactions. By demonstrating how governance instruments such as signage reflect deeper social, environmental, and political dynamics, this study advances our theoretical understanding of “design governance” and its role in urban sustainability. We discuss practical implications for municipalities seeking to foster coexistence through clear, behaviorally informed signage. Full article

Stance detection on social media is increasingly vital for understanding public opinion, mitigating misinformation, and enhancing digital trust. This study proposes a modular Multi-Task Learning (MTL) framework that jointly models stance detection and sentiment analysis to address the emotional complexity of user-generated content. The architecture integrates a RoBERTa-based shared encoder with BiCARU layers to capture both contextual semantics and sequential dependencies. Stance classification is reformulated into three parallel binary subtasks, while sentiment analysis serves as an auxiliary signal to enrich stance representations. Attention mechanisms and contrastive learning are incorporated to improve interpretability and robustness. Evaluated on the NLPCC2016 Weibo dataset, the proposed model achieves an average F1-score of 0.7886, confirming its competitive performance in emotionally nuanced classification tasks. This approach highlights the value of emotional cues in stance inference and offers a scalable, interpretable solution for secure opinion mining in dynamic online environments. Full article

Based on Agenda-Setting Theory, Media Agenda Synergy (MAS) can enhance the communication effectiveness of public issues (e.g., climate change, social justice, and public health) through the information resonance and agenda complementarity among cross-media platforms, thus reconstructing the public perception. In this paper, we focus on the dynamic impact of cross-media agenda synergy on public agenda intensity and innovatively propose a “HMM-Granger” hybrid modeling framework for Media Agenda Synergy: Firstly, we quantify the causal weights of agenda shifting based on the deconstruction of the nonlinear time-series dependence of multisource media data by using LSTM neural networks. Secondly, the state transfer probability matrix of the Hidden Markov Model reveals the dual paths of “explicit collaboration” (e.g., issue resonance) and “implicit competition” (e.g., agenda masking) in media agenda coordination. The results of this study show that the Agenda Synergy between mainstream media and social media during major events can generate an Agenda Multiplier Effect, resulting in a significant increase in the intensity of the public agenda. This study provides a computable theoretical paradigm for Inter-Media Agenda Network modeling and data-driven decision support for optimizing opinion guidance strategies. Full article

With the rapid development of social media, short-text data have become increasingly important in fields such as public opinion monitoring, user feedback analysis, and intelligent recommendation systems. However, existing short-text sentiment analysis models often suffer from limited cross-domain adaptability and poor generalization performance. To address these challenges, this study proposes a novel short-text sentiment classification model based on the Bidirectional Encoder Representations from Transformers (BERTs) and a dual-stream Transformer gated attention mechanism. This model first employs Bidirectional Encoder Representations from Transformers (BERTs) and the Chinese Robustly Optimized BERT Pretraining Approach (Chinese-RoBERTa) to achieve data augmentation and multilevel semantic mining, thereby expanding the training corpus and enhancing minority class coverage. Second, a dual-stream Transformer gated attention mechanism was developed to dynamically adjust feature fusion weights, enhancing adaptability to heterogeneous texts. Finally, the model integrates a Bidirectional Gated Recurrent Unit (BiGRU) with Multi-Head Self-Attention (MHSA) to strengthen sequence information modeling and global context capture, enabling the precise identification of key sentiment dependencies. The model’s superior performance in handling data imbalance and complex textual sentiment logic scenarios is demonstrated by the experimental results, achieving significant improvements in accuracy and F1 score. The F1 score reached 92.4%, representing an average increase of 8.7% over the baseline models. This provides an effective solution for enhancing the performance and expanding the application scenarios of short-text sentiment analysis models. Full article

The increasing integration of artificial intelligence agents (AIAs) based on large language models (LLMs) is transforming many spheres of society. These agents act as human assistants, forming Distributed Intelligent Systems (DISs) and engaging in opinion formation, consensus-building, and collective decision-making. However, complex DIS network topologies introduce significant uncertainty into these processes. We propose a quantum-inspired graph signal processing framework to model collective behavior in a DIS interacting with an external environment represented by an influence matrix (IM). System topology is captured using scale-free and Watts–Strogatz graphs. Two contrasting interaction regimes are considered. In the first case, the internal structure fully aligns with the external influence, as expressed by the commutativity between the adjacency matrix and the IM. Here, a renormalization-group-based scaling approach reveals minimal reservoir influence, characterized by full phase synchronization and coherent dynamics. In the second case, the IM includes heterogeneous negative (antagonistic) couplings that do not commute with the network, producing partial or complete spectral disorder. This disrupts phase coherence and may fragment opinions, except for the dominant collective (Perron) mode, which remains robust. Spectral entropy quantifies disorder and external influence. The proposed framework offers insights into designing LLM-participated DISs that can maintain coherence under environmental perturbations. Full article

This study investigates the evolution of online public opinion during the COVID-19 pandemic by integrating topic mining with sentiment analysis. To overcome the limitations of traditional short-text models and improve the accuracy of sentiment detection, we propose a novel hybrid framework that combines a GloVe-enhanced Biterm Topic Model (BTM) for semantic-aware topic clustering with a RoBERTa-TextCNN architecture for deep, context-rich sentiment classification. The framework is specifically designed to capture both the global semantic relationships of words and the dynamic contextual nuances of social media discourse. Using a large-scale corpus of more than 550,000 Weibo posts, we conducted comprehensive experiments to evaluate the model’s effectiveness. The proposed approach achieved an accuracy of 92.45%, significantly outperforming baseline transformer-based baseline representative of advanced contextual embedding models across multiple evaluation metrics, including precision, recall, F1-score, and AUC. These results confirm the robustness and stability of the hybrid design and demonstrate its advantages in balancing precision and recall. Beyond methodological validation, the empirical analysis provides important insights into the dynamics of online public discourse. User engagement is found to be highest for the topics directly tied to daily life, with discussions about quarantine conditions alone accounting for 42.6% of total discourse. Moreover, public sentiment proves to be highly volatile and event-driven; for example, the announcement of Wuhan’s reopening produced an 11% surge in positive sentiment, reflecting a collective emotional uplift at a major turning point of the pandemic. Taken together, these findings demonstrate that online discourse evolves in close connection with both societal conditions and government interventions. The proposed topic–sentiment analysis framework not only advances methodological research in text mining and sentiment analysis, but also has the potential to serve as a practical tool for real-time monitoring online opinion. By capturing the fluctuations of public sentiment and identifying emerging themes, this study aims to provide insights that could inform policymaking by suggesting strategies to guide emotional contagion, strengthen crisis communication, and promote constructive public debate during health emergencies. Full article

Despite the growing impacts of climate change worldwide, achieving consensus on climate action remains a challenge partly because of heterogeneity in perceptions of climate risks within and across countries. Lack of consensus has hindered global collective action. We use a system dynamics approach to examine how interactions among cultural, socio-political, psychological, and institutional factors shape public support or opposition for climate mitigation policy. We investigate the conditions under which the dominant public opinion about climate policy can shift within a 20-year time frame. We observed opinion shifts in 20% of simulations, primarily in individualistic cultural contexts with high perceived climate risk. Changing the dominant opinion was especially difficult to achieve in collectivistic cultures, as we observed no shifts in dominant opinion within the parameter ranges examined. Our study underscores the importance of understanding how cultural context mediates the approaches needed to effectively mobilize collective climate action. Full article

In the current digital era, as global industries transform due to technological advancements, tracking trends in emerging services has assumed increased significance. This study proposes an innovative model that integrates circular picture fuzzy sets (CPFSs) with the Weighted Aggregated Sum Product Assessment (WASPAS) method to evaluate and rank various AI-driven trends within the service industry. The CPFS approach offers enhanced responses to uncertainty, symmetric information, indecision, and varying expert opinions, while the WASPAS method ensures a dependable system for ranking prominent trends. To facilitate the evaluation process, experts and relevant studies were consulted to establish criteria that address technological developments, organizational dynamics, and market fluctuations. A hybrid fuzzy Multi-Criteria Decision-Making (MCDM) framework enabled the analysis of several potential innovations related to AI and their prioritization in the context of digitalized sectors, including healthcare, finance, online shopping, retail, and logistics. This framework is a well-structured and flexible tool for professionals and policymakers seeking to navigate the challenges of identifying new trends within unpredictable digital environments. The findings indicate that the circular picture fuzzy WASPAS approach significantly enhances trend prioritization and fosters strategic thinking in digital innovation. Furthermore, the research provides valuable insights into the complexities of fuzzy decision-making and the promotion of AI-based innovation management. Full article

This work addresses the broken symmetry in syntactic–semantic representations for Aspect-Based Sentiment Analysis, where advancements have been driven by the use of pre-trained language models to achieve contextual understanding and graph neural networks capturing aspect–opinion dependencies using syntactic trees. However, long-distance aspect–opinion pairs pose challenges: the structural noise in dependency trees often causes erroneous associations, while the discrete structure of the constituent trees leads to constituent fragmentation. In this paper, we propose DySynGAT and introduce a Localized Graph Attention Network (LGAT) to fuse bi-gram syntactic and semantic information from both dependency and constituent trees, effectively mitigating interference from dependency tree noise. A dynamic semantic enhancement module efficiently integrates local and global semantics, alleviating constituent fragmentation caused by constituent trees. An aspect–context interaction graph (ACIG), built upon minimal semantic segmentation and jointly enhanced features, filters out noisy cross-clause edges. Spatial reduction attention (SRA) with mean pooling compresses the redundant sequential features, reducing the noise under long-range dependencies. Experiments on foods and beverages, electronics, and user review datasets demonstrate F1 score improvements of 0.55%, 3.55%, and 1.75% over SAGAT-BERT, demonstrating strong cross-domain robustness. Full article

Political polarization in Western democracies has accelerated in the last decade, with negative social consequences. Research across disciplines on antecedents, manifestations and societal impacts is hindered by social systems’ complexity: their constant flux impedes tracing causes of observed trends and prediction of consequences, hampering their mitigation. Social physics models exploit a characteristic of complex systems: what seems chaotic at one observation level may exhibit patterns at a higher level. Therefore, dynamic modeling of complex systems allows anticipation of possible events. We use this approach to anticipate 2024 US election results. We consider the highly polarized Democrats and Republicans, and Independents fluctuating between them. We generate average group-stance scenarios in time and explore how polarization and depolarization might have affected 2024 voting outcomes. We find that reducing polarization might advantage the larger voting group. We also explore ways to reduce polarization, and their potential effects on election results. The results inform regarding the perils of polarization trends, and on possibilities of changing course. Full article

Achieving consensus in group decision-making often involves overcoming significant challenges, particularly reconciling diverse perspectives and mitigating biases hindering agreement. Traditional methods relying on human facilitators are usually constrained by scalability and efficiency, especially in large-scale, fast-paced discussions. To address these challenges, this study proposes a novel real-time facilitation framework, employing large language models (LLMs) as automated facilitators within a custom-built multi-user chat system. This framework is distinguished by its real-time adaptive system architecture, which enables dynamic adjustments to facilitation strategies based on ongoing discussion dynamics. Leveraging cosine similarity as a core metric, this approach evaluates the ability of three state-of-the-art LLMs—ChatGPT 4.0, Mistral Large 2, and AI21 Jamba-Instruct—to synthesize consensus proposals that align with participants’ viewpoints. Unlike conventional techniques, the system integrates adaptive facilitation strategies, including clarifying misunderstandings, summarizing discussions, and proposing compromises, enabling the LLMs to refine consensus proposals based on user feedback iteratively. Experimental results indicate that ChatGPT 4.0 achieved the highest alignment with participant opinions and required fewer iterations to reach consensus. A one-way ANOVA confirmed that differences in performance between models were statistically significant. Moreover, descriptive analyses revealed nuanced differences in model behavior across various sustainability-focused discussion topics, including climate action, quality education, good health and well-being, and access to clean water and sanitation. These findings highlight the promise of LLM-driven facilitation for improving collective decision-making processes and underscore the need for further research into robust evaluation metrics, ethical considerations, and cross-cultural adaptability. Full article

In this paper, we have constructed a closed-form optimal strategy within a social network using stochastic McKean–Vlasov dynamics. Each agent independently minimizes their dynamic cost functional, driven by stochastic differential opinion dynamics. These dynamics reflect agents’ opinion differences from others and their past opinions, with random influences and stubbornness adding to the volatility. To gain an analytic insight into the optimal feedback opinion, we employed a Feynman-type path integral approach with an appropriate integrating factor, marking a novel methodology in this field. Additionally, we utilized a variant of the Friedkin–Johnsen-type opinion dynamics to derive a closed-form optimal strategy for an agent and conducted a comparative analysis. Full article