Search Results (135)

Social network content is increasingly used as an auxiliary evidence stream for financial monitoring, risk assessment, and short-horizon decision support, yet many reported gains are hard to interpret because observability, timing, and attribution are handled inconsistently across studies. This review critically synthesizes the end-to-end pipeline that transforms social posts, interaction traces, linked artifacts, and related signals into decision-facing indicators, emphasizing evidence provenance, sampling bias, conditioning (bot/spam filtering, entity linking, timestamp alignment), and the modeling blocks typically used (text, temporal, relational, and fusion components) under deployment constraints. Across sentiment, relational, and multimodal or cross-platform signals, the analysis finds that apparent improvements often depend more on alignment discipline and conservative attribution than on architectural novelty, and that performance can be inflated by attention confounds, temporal leakage, and visibility effects. Relational indicators are most defensible for monitoring coordination and propagation patterns, while multimodal gains require clear ablations and realistic missing-modality tests. To support decision readiness, the paper consolidates assurance requirements covering manipulation, degraded observability, calibration and traceability, and provides compact reporting checklists and failure-mode mitigations. Overall, the review supports bounded claims and argues for time-aware evaluation and auditable pipelines as prerequisites for operational use. Full article

Accurate one-step forecasting of scenic-spot visitor demand is challenging due to strong non-stationarity, holiday-induced peaks, and abrupt reputation-driven shocks. We propose a symmetry-aware dynamic graph learning framework that fuses social–physical sensing streams for robust demand prediction. Online reviews are treated as social sensing, transformed into daily sentiment indicators, and aligned with demand using a delay-aware aggregation scheme. To capture evolving inter-spot dependencies, we construct a time-varying adjacency matrix that is updated over time and integrated into a lightweight spatio-temporal forecasting model, Dynamic Spatio-temporal Graph Attention LSTM (DSGAT-LSTM). The model preserves the permutation-invariant property of graph learning while introducing sentiment-guided feature reweighting and sentiment-gated temporal updates to better track volatility. Experiments on multi-year daily data from multiple A-level scenic spots with holiday and weather context demonstrate consistent error reductions over representative temporal and graph-based baselines, together with improved stability under peak and shock conditions. We will release the processed feature-level dataset and implementation scripts to support reproducibility. Full article

Accurate forecasting of nutrition supply–demand dynamics is essential for reducing resource wastage and improving equitable allocation. However, this task remains challenging due to heterogeneous data sources, cold-start regions, and the risk of information leakage in spatiotemporal modeling. This study presents a leakage-aware multimodal machine learning framework for nutrition supply–demand forecasting. The framework integrates temporal, spatial, and contextual information within a unified architecture. It combines self-supervised temporal representation learning, causal time-lag modeling, and few-shot adaptation to improve generalization under limited or previously unseen data conditions. Heterogeneous inputs include epidemiological, environmental, demographic, sentiment, and biologically derived indicators. These signals are encoded using a PatchTST-inspired temporal backbone coupled with a feature-token transformer employing cross-modal attention. Spatial dependencies are explicitly modeled using graph neural networks. Hierarchical decoding enables multi-horizon forecasting with calibrated uncertainty estimates. Model evaluation is conducted under strict spatiotemporal hold-out protocols with explicit leakage detection. All synthetic signals are excluded from testing. Across geographically and temporally disjoint datasets, the proposed framework consistently outperforms strong unimodal and multimodal baselines. It achieves macro-F1 scores above 99.5% and stable early-warning lead times of approximately 9 days under distribution shift. Ablation studies indicate that causal time-lag enforcement and few-shot adaptation contribute most strongly to performance robustness. Closed-loop simulation experiments suggest potential reductions in nutrient wastage of approximately 38%, response latency of 19%, and operational costs of 16% when deployed as a decision-support tool. External validation on fully unseen regions confirms the generalizability of the framework under realistic forecasting constraints. Full article

The rapid expansion of global e-commerce platforms has led to unprecedented volumes of heterogeneous, multimodal, and continuously evolving data, creating significant challenges for prediction, personalization, trust, and operational decision-making. Deep Learning has emerged as a core enabling technology for addressing these challenges, offering powerful representation learning, sequential reasoning, graph-based inference, and decision-centric optimization capabilities. This survey provides a comprehensive and decision-oriented review of recent advances in Deep Learning for e-commerce, covering consumer behavior prediction, demand forecasting, recommendation systems, sentiment and review intelligence, catalogue understanding, fraud detection, cybersecurity, and large-scale operational optimization. Beyond predictive and personalization tasks, the survey emphasizes decision intelligence, highlighting the growing role of Reinforcement Learning and integrated Artificial Intelligence systems in pricing, logistics, warehouse automation, and platform reliability. We organize the literature according to key e-commerce objectives and operational contexts, analyze methodological trends and deployment challenges, and discuss limitations related to scalability, robustness, interpretability, and cross-border adaptability. Finally, we identify open research directions toward unified multimodal foundation models, culturally adaptive intelligence, and trustworthy, sustainable Artificial Intelligence systems for next-generation e-commerce platforms. Full article

Recommender systems represent an essential infrastructure for digital platforms. To understand their evolution, we analyze 15,944 Web of Science publications (1980–2025) using bibliometric techniques, generative and transformer models for sentiment analysis and latent topic modeling. Our analysis yields three major findings. First, e-commerce recommendation research exhibits rapid growth in advanced representation techniques, with compound annual growth rates for contrastive learning (187%), graph neural networks (89%) and federated learning (72%). Second, algorithmic fairness and privacy preservation have emerged as critical research directions. Third, collaborative networks indicate a geographical shift, with Asia–Pacific regions becoming influential research hubs. The methodology integrates CAGR analysis with Latent Dirichlet Allocation (LDA, coherence score = 0.687) and BERTopic for thematic mapping and network analysis. Additionally, we employ sentiment analysis (VADER, TextBlob and a sentiment analysis pipeline from Hugging Face Transformers) and temporal heatmaps to capture research narratives. Topic modeling with LDA identifies five core themes: (1) Collaborative Filtering; (2) Machine Learning and Educational Systems; (3) Web Services and Business Applications; (4) Content and Multimedia Recommendations; (5) Graph Neural Networks and Advanced Models. BERTopic provides eight more nuanced themes based on semantics. Citation patterns follow the Pareto principle, where the top 1% of articles account for 29.1% of all citations, confirming a highly skewed impact distribution. Notably, established keywords show declining trajectories, indicating a methodological evolution toward newer, deep learning and generative AI-based paradigms. Full article

Aspect Sentiment Triplet Extraction (ASTE) aims to jointly identify aspect terms, opinion terms, and their associated sentiment polarities. Existing approaches, such as tagging or span-based modeling, often struggle with complex aspect–opinion interactions and long-distance dependencies. We propose a Knowledge-Augmented Graph Convolutional Network (KMG-GCN) that represents a sentence as a multi-channel graph integrating syntactic dependencies, part-of-speech tags, and positional relations. An adjacency tensor is constructed via a biaffine attention mechanism, while a multi-anchor triplet learning strategy with orthogonal projection enhances representation disentanglement. Furthermore, a pairwise refinement module explicitly models aspect–opinion associations, improving robustness against overlapping triplets. Experiments on multiple benchmarks demonstrate that KMG-GCN achieves state-of-the-art performance with improved efficiency and generalization. Full article

This study investigates how emotionally structured online interaction networks shape consumer perception of new energy vehicle (NEV) technology. Drawing on discussion forum data from two leading NEV brands, Brand_T and Brand_B, we focus on how users respond to brand technological narratives and how these responses translate into distinct patterns of peer-to-peer interaction. Using a behavioral network analysis framework, we integrate sentiment analysis, topic modeling, and Exponential Random Graph Modeling (ERGM) to uncover the psychological and structural mechanisms underlying consumer engagement. Three main findings emerge. First, users display brand-specific emotional-cognitive profiles: Brand_T communities show broader technological engagement but more heterogeneous emotional responses, whereas Brand_B communities exhibit more emotionally aligned discussions. Second, emotional homophily is a robust driver of interaction ties, particularly in Brand_B forums, where positive sentiment clusters into dense and supportive discussion subnetworks. Third, perceived technological benefits, rather than risk sensitivity, are consistently associated with higher interaction intensity, underscoring the motivational salience of anticipated gains over cautionary concerns in shaping engagement behavior. The study contributes to behavioral science and transportation behavior research by linking consumer sentiment, cognition, and social interaction dynamics in digital environments, offering an integrated theoretical account that bridges the Elaboration Likelihood Model, social identity processes, and behavioral network formation. This advances the understanding of technology perception from static individual evaluations to dynamic, group-structured outcomes. It highlights how emotionally patterned interaction networks can reinforce or recalibrate technology-related perceptions, offering practical implications for NEV manufacturers and policymakers seeking to design psychologically informed communication strategies that support sustainable technology adoption. Full article

This paper presents the design, implementation and evaluation of an agentic virtual assistant (VA) for a medical clinic, combining large language models (LLMs) with retrieval-augmented generation (RAG) technology and multi-agent artificial intelligence (AI) frameworks to enhance reliability, clinical accuracy and explainability. The assistant has multiple functionalities and is built around an orchestrator architecture in which a central agent dynamically routes user queries to specialized tools for retrieval-augmented question answering (Q&A), document interpretation and appointment scheduling. The implementation combines LangChain and LangGraph with interactive visualizations to track reasoning steps, prompts using Gemini 2.5 Flash defines tool usage and strict formatting rules, maintaining reliability and mitigating hallucinations. Prompt engineering has an important role in the implementation and thus, it is designed to assist the patient in the human–computer interaction. Evaluation through qualitative and quantitative metrics, including ROUGE, BLEU, LLM-as-a-judge and sentiment analysis, confirmed that the multi-agent architecture enhances interpretability, accuracy and context-aware performance. Evaluation shows that the multi-agent architecture improves reliability, interpretability and alignment with medical requirements, supporting diverse clinical tasks. Furthermore, the evaluation shows that Gemini 2.5 Flash combined with clinic-specific RAG significantly improves response quality, grounding and coherence compared with earlier models. SBERT analyses confirm strong semantic alignment across configurations, while LLM-as-a-judge scores highlight the superior relevance and completeness of the 2.5 RAG setup. Although some limitations remain, the updated system provides a more reliable and context-aware solution for clinical question answering. Full article

This work addresses the problem of demand forecasting in supply chain management, where the consolidation of scattered and heterogeneous data and the lack of precise forecasting methods generate operational inefficiencies, resulting in increased backorders and high inventory costs. To tackle these challenges, we propose a novel Decision Support System that jointly integrates an intelligent processing engine based on Graph Neural Networks (GNNs) for time series forecasting. Our approach lies in explicitly modeling the demand prediction task as a Multivariate Time Series forecasting problem on a causal dependency graph. Specifically, we use a GCN to process a graph where the nodes represent the target demand and key exogenous variables (Consumer Sentiment Index, Consumer Price Index, Personal Income, and Unemployment Rate), and the edges explicitly encode the interdependencies and causal relationships among these economic factors and demand. Unlike previous applications of GNNs in supply chain management, which typically focus on inventory networks or single-factor interactions, our approach uses GCN to dynamically capture the temporal interactions among multiple macroeconomic and internal series on future demand. We compare our method with other machine learning algorithms for demand forecasting. In the experiments conducted, the proposed GCN approach can accurately predict the abrupt changes that appear in demand behavior over time, whereas the other comparison methods tend to excessively smooth these transitions. Full article

Multimodal sentiment analysis jointly exploits textual, acoustic, and visual signals to recognize human emotions more accurately than unimodal models. However, real-world data often contain noisy or partially missing modalities, and naive fusion may allow unreliable signals to degrade overall performance. To address this, we propose an enhanced framework named MISA-GMC, a lightweight extension of the widely used MISA backbone that explicitly accounts for modality reliability. The core idea is to adaptively reweight modalities at the sample level while regularizing cross-modal representations during training. Specifically, a reliability-aware gated fusion module down-weights unreliable modalities, and two auxiliary training-time regularizers (momentum contrastive learning and a lightweight correlation graph) help stabilize and refine multimodal representations without adding inference-time overhead. Experiments on three benchmark datasets—CMU-MOSI, CMU-MOSEI, and CH-SIMS—demonstrate the effectiveness of MISA-GMC. For instance, on CMU-MOSI, the proposed model improves 7-class accuracy from 43.29 to 45.92, reduces the mean absolute error (MAE) from 0.785 to 0.712, and increases the Pearson correlation coefficient (Corr) from 0.764 to 0.795. This indicates more accurate fine-grained sentiment prediction and better sentiment-intensity estimation. On CMU-MOSEI and CH-SIMS, MISA-GMC also achieves consistent gains over MISA and strong baselines such as LMF, ALMT, and MMIM across both classification and regression metrics. Ablation studies and missing-modality experiments further verify the contribution of each component and the robustness of MISA-GMC under partial-modality settings. Full article

Traditional Environmental, Social, and Governance (ESG) assessments rely heavily on corporate disclosures and third-party ratings, which are often delayed, inconsistent, and prone to bias. These limitations leave stakeholders without timely visibility into rapidly evolving ESG events. These assessment frameworks also fail to capture the dynamic nature of ESG issues reflected in public news media. This research addresses these limitations by proposing and implementing an automated framework utilising Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Knowledge Graphs (KG), to analyse ESG news data for companies listed on major stock indices. The methodology involves several stages: collecting a registry of target companies; retrieving relevant news articles; applying Named Entity Recognition (NER), sentiment analysis, and ESG domain classification; and constructing a linked property knowledge graph to structure the extracted information semantically. The framework culminates in an interactive dashboard for visualising and querying the resulting graph database. The resulting knowledge graph supports comparative inferential analytics across indices and sectors, uncovering divergent ESG sentiment profiles and thematic priorities that traditional reports overlook. The analysis also reveals comparative insights into sentiment trends and ESG focus areas across different exchanges and sectors, offering perspectives often missing from traditional methods. Findings indicate differing ESG sentiment profiles and thematic focuses between the UK (FTSE) and Australian (ASX) indices within the analysed dataset. This study confirms AI/KG’s potential for a modular, dynamic, and semantically rich ESG intelligence approach, transforming unstructured news into interconnected insights. Limitations and areas for future work, including model refinement and integration of financial data, are also discussed. This proposed framework augments traditional ESG evaluations with automated, scalable, and context-rich analysis. Full article

Social networks have emerged as integral platforms for communication and information dissemination in contemporary society. The spread of negative sentiments and its impact on activities of users in social networks is a crucial issue. When users receive negative reviews about news or articles, regardless of authenticity, they form opinions based on their own understanding, and statistics show that more than 90% of the time this reveals predictable behavior patterns. To address this situation, the proposed Antecedents of Negative Sentiment through Expression Capacity: Mixed Methods (ANSEC-MM) study identifies the antecedents of negative sentiment using expression capacity as a mixed-methods approach to mitigate the generation of negative sentiments. The proposed model introduces the concept of identification of influencer nodes with further categorization into active and inactive influencer nodes. The model separates negative influencer nodes from positive nodes and processes the negative influencer nodes further. A Node Expressive Capacity (NE) metric predicts the frequency with which users interact with neighboring influencer nodes, which contributes to the generation of negative sentiments. A Cognitive Effect Coefficient (φ) defines the temperament status of the users. Through further computation, the model distinguishes the proportion of negative sentiments from positive ones. Negative sentiment mitigation is achieved through a developed algorithmic approach. Performance is tested and compared across three datasets against state-of-the-art models: EANN, BERT, and AOAN. The proposed model demonstrated superior performance in negative sentiment detection and mitigation, achieving accuracy rates of 90% and 88%, respectively, compared to existing models. Full article

Forecasting stock prices remains a central challenge in financial modelling, as markets are influenced by market sentiment, firm-level fundamentals and complex interactions between macroeconomic and microeconomic factors, for example. This study evaluates the predictive performance of both classical statistical models and advanced attention-based deep learning architectures for daily stock price forecasting. Using a dataset of major U.S. equities and Exchange Traded Funds (ETFs) covering 2012–2024, we compare traditional statistical approaches, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ES) in the Error, Trend, Seasonal (ETS) framework, with deep learning architectures such as the Temporal Fusion Transformer (TFT), and a novel hybrid model, the TFT-Graph Neural Network (TFT-GNN), which incorporates relational information between assets. All models are assessed under consistent experimental conditions in terms of forecast accuracy, computational efficiency, and interpretability. Our results indicate that while statistical models offer strong baselines with high stability and low computational cost, the TFT outperforms them in capturing short-term nonlinear dependencies. The hybrid TFT-GNN achieves the highest overall predictive accuracy, demonstrating that relational signals derived from inter-asset connections provide meaningful enhancements beyond traditional temporal and technical indicators. These findings highlight the advantages of integrating relational learning into temporal forecasting frameworks and emphasise the continued relevance of statistical models as interpretable and efficient benchmarks for evaluating deep learning approaches in high-frequency financial prediction. Full article

The rapid growth of user-generated content in the digital space has increased the necessity of properly and interpretively analyzing sentiment and emotion systems. This research paper presents a new hybrid model, HAM (Hybrid Attention-based Model), a Transformer-based contextual embedding model combined with deep sequential modeling and multi-layer explainability. The suggested framework integrates the BERT/RoBERTa encoders, Bidirectional LSTM, and Graph Attention that can be used to embrace semantic and aspect-level sentiment correlation. Additionally, an enhanced Explainability Module, including Attention Heatmaps, Aspect-Level Interpretations, and SHAP/Integrated Gradients analysis, contributes to the increased model transparency and interpretive reliability. Four benchmark datasets, namely GoEmotions-1, GoEmotions-2, GoEmotions-3, and Amazon Cell Phones and Accessories Reviews, were experimented on in order to have a strong cross-domain assessment. The 28 emotion words of GoEmotions were merged into five sentiment-oriented classes to harmonize the dissimilarity in the emotional granularities to fit the schema of the Amazon dataset. The proposed HAM model had a highest accuracy of 96.4% and F1-score of 94.9%, which was significantly higher than the state-of-the-art baselines like BERT (89.8%), RoBERTa (91.7%), and RoBERTa+BiLSTM (92.5%). These findings support the idea that HAM is a better solution to finer-grained emotional details and is still interpretable as a vital move towards creating open, exposible, and domain-tailored sentiment intelligence systems. Future endeavors will aim at expanding this architecture to multimodal fusion, cross-lingual adaptability, and federated learning systems to increase the scalability, generalization, and ethical application of AI. Full article

In Mongolian Text Sentiment Analysis (MTSA), the scarcity of annotated sentiment datasets and the insufficient consideration of syntactic dependency and topological structural information pose significant challenges to accurately capturing semantics and effectively extracting emotional features. To address these issues, this paper proposes a Mongolian Text Sentiment Analysis model based on ConvBERT and Graph Attention Network (MTSA-CG). Firstly, the ConvBERT pre-trained model is employed to extract textual features under limited data conditions, aiming to mitigate the shortcomings caused by data scarcity. Concurrently, textual data are transformed into graph-structured data, integrating co-occurrence, dependency, and similarity information into a Graph Attention Network (GAT) to capture syntactic and structural cues, enabling a deeper understanding of semantic and emotional connotations for more precise sentiment classification. The proposed multi-graph fusion strategy employs a hierarchical attention mechanism that dynamically weights different graph types based on their semantic relevance, distinguishing it from conventional graph aggregation methods. Experimental results demonstrate that, in comparison with various advanced baseline models, the proposed method significantly enhances the accuracy of MTSA. Full article