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Background: In 2024, Reddit, an emerging social media platform, saw a 50% increase in monthly users to nearly 100 million. Reddit has also emerged as a significant space for discussions about health conditions, including epilepsy, which affects about 50 million people globally. Purpose: This study aims to explore trends in the volume, timing, themes, emotional tone, and sentiment of posts on the r/Epilepsy subreddit from 1 December 2023 to 31 December 2024. Methods: We collected 25,222 original English-language posts from r/Epilepsy using Reddit’s Application Programming Interface (API). Data extraction was restricted to English-language submissions to ensure compatibility with sentiment and thematic analyses. We analyzed post volume and timing using chi-square tests and Poisson regression. Emotional tone was measured using TextBlob (version 0.19.0), while compound sentiment scores were calculated via VADER (Valence Aware Dictionary and Sentiment Reasoner) (NLTK version 3.9.1). A Pearson correlation assessed agreement between sentiment and emotional tone, with statistical significance set at p < 0.05. Thematic analysis was conducted using a KMeans clustering algorithm (scikit-learn version 1.6.1) to identify recurring discussion topics. Results: Total monthly posts steadily increased, with the highest number (2175) in December 2024. Peak posts in descending order were in December 2024, August 2024, and November 2024. Posts were not evenly distributed across the week, with a significant peak on Mondays (χ² = 86.75, p < 0.001) and Poisson regression confirming higher activity early in the week (p = 0.001). Emotional tones fluctuated, with positive sentiments in January and October 2024, and negative sentiments in March and August 2024. KMeans clustering identified five main themes: treatment experiences, community engagement, personal experiences, solidarity, and subreddit gratitude. Manual validation of a random subset of posts demonstrated moderate concordance between automated sentiment classification and human ratings. Conclusions: This study highlights temporal patterns, sentiment dynamics, and thematic structure in online discussions on epilepsy. Social media may offer valuable, real-time insights into patient-centered concerns and community engagement, which can inform healthcare professionals and advocacy groups in supporting individuals affected by epilepsy. Future studies may compare trends of epilepsy discussions across various social media platforms, such as X and Instagram, to further understand online patient experiences. Full article

Accurate gold price forecasting is crucial for economic stability and investment decision-making. In order to improve the accuracy of gold price prediction and quantify the uncertainty of gold price fluctuation, this paper proposes a hybrid model (CNN-QRLSTM) that integrates convolutional neural network (CNN) and quantile regression long- and short-term memory network (QRLSTM) and innovatively introduces news text data to quantify the media sentiment. We combine EEMD with the Hurst index to remove white noise from the original signal, and the processed data is used as the input layer of the prediction model. Furthermore, to demonstrate the impact of news sentiment on gold prices, this paper employs entropy measurement methods based on information theory to quantify the uncertainty and information content embedded within processed gold price sequences and derived sentiment indicators. The mutual information (MI) algorithm, based on information entropy, captures the nonlinear correlations between financial keywords and market sentiment. It constructs a financial sentiment lexicon (covering keywords such as economic policies and geopolitical conflicts), combines semantic rules with context-weighted strategies, calculates sentiment scores for news texts, and generates daily aggregated media sentiment indicators. This entropy-based perception method not only enhances the interpretability of emotion-driven fluctuations but also provides a theoretical foundation for reducing prediction uncertainty through multi-source data fusion. The experiment uses 2022–2025 daily London gold spot price data, Shanghai Gold Exchange gold price data, and the same period of Gold Investment Network gold market news to carry out the study. The empirical study shows that the synergy of multi-source data fusion and the quantile regression mechanism can improve the accuracy of gold price prediction and the new paradigm of risk interpretation while providing theoretical support for the formulation of quantitative investment strategies. Full article

Aligning system design with evolving user expectations remains a critical challenge in contemporary digital markets. While online reviews offer vast potential for informing product development, transforming unstructured feedback into actionable system intelligence requires rigorous analytical frameworks. This study proposes a unified framework that synthesizes topic-related text analysis, sentiment analysis, and time-series trends to model user requirements as indicators of multidimensional system value. Based on this framework, we introduce the Product Online User Perception Score to quantify user perception of product attributes through the integration of attention, discussion richness, and sentiment. Crucially, a User Requirement Value model is developed to assess the strategic priority of requirements. The model applies a discussion richness dimension to filter superficial noise and employs a reverse valuation mechanism to identify systematic gaps between high attention and low satisfaction. Comparative evidence from the Chinese automotive market highlights the evolution of user needs during the transition from fuel-powered to new energy vehicles. While manufacturers prioritize enterprise-centric intelligent features, user dissatisfaction is systematically concentrated on basic ergonomic deficits, revealing that foundational operational value remains a prerequisite for overall system success. This study shifts the analytical paradigm from descriptive monitoring to diagnostic system valuation, providing a measurable and diagnostic instrument for supporting evidence-based product iteration. Full article

The limited availability of structured and consistent health-facility information poses challenges for assessing service accessibility and quality in rapidly growing cities, particularly in the Middle East. Although digital map platforms provide extensive public data, such information is often fragmented and not directly suitable for systematic spatial analysis. This study presents GeoJed, a framework designed to automate the collection, organisation, and spatial analysis of healthcare facility information from digital map platforms. The framework is demonstrated through a case study in Jeddah, Saudi Arabia, highlighting its applicability for large-scale and reproducible spatial analysis of healthcare services. Using the resulting GeoJedHF dataset, a baseline analysis was conducted to illustrate the analytical value of the collected data, including the construction of an initial Patient Satisfaction Index (PSI) that integrates service availability with user-reported quality indicators derived from a multilingual sentiment model (XLM-RoBERTa). The results reveal clear spatial variations between districts in both facility distribution and perceived service quality. Overall, GeoJed establishes a reusable and extensible process for facility-level spatial data acquisition and analysis, with potential applications in accessibility assessment, urban planning, and service evaluation. Full article

This article examines Romanian internet memes as cultural micro-narratives that encode social critique, identity negotiation, and emotional response through compressed, multimodal storytelling. Using a mixed-method approach, the study integrates qualitative narrative analysis with quantitative sentiment data drawn from the RoMEMESv2 corpus, comprising 983 Romanian-language memes. The analysis identifies recurrent narrative roles and plot structures adapted from Propp’s morphology and applied to digital contexts, revealing archetypal roles, such as the slacker hero, the bureaucratic villain, the domestic guardian, and the trickster. From a quantitative point of view, the corpus exhibits a dominant negative sentiment, particularly within political memes, which combine systemic critique with affective ambivalence. These findings distinguish Romanian memes from datasets in other languages, suggesting that negativity functions not as deviance, but as a culturally specific narrative and emotional resource. Multimodal analysis demonstrates how visual and textual elements operate through anchorage, intertextuality, and symbolic compression, so as to construct narrative messages within single frames. The study argues that Romanian memes function as digital folklore: they narrate social frustration and institutional distrust through irony, repetition, and archetypal condensation, offering insights into the emotional and narrative logic of post-communist digital culture. Full article

The purpose of this paper is to analyze the media visibility of investments implemented in Wrocław, with a particular focus on the democratization of urban processes through the Wrocław Participatory Budget (WPB) and to study the public perception of these projects within the local information landscape. The paper presents an integrated analytical methodology combining geospatial data from the Spatial Information System of Wrocław (SIP) with textual data from the full corpus of local news articles from Wrocław. A hybrid data processing pipeline was used, including filtering of articles about Wrocław, geoparsing of location names, matching articles to investments using classic Term Frequency-Inverse Document Frequency (TF-IDF) models and embedding in language models such as HerBERT, and sentiment analysis using the XLM-T model. The results reveal strong imbalances in the visibility of WPB projects, that almost 90% of investments were not mentioned even once in the media. Temporal sentiment analysis indicated differences between categories of WPB projects. The results confirm the existence of “media deserts” and “islands of attention,” which leads to information exclusion for specific local communities and marginalized groups. This translates into asymmetry in residents’ knowledge of the real scope of the WPB program. The paper emphasizes the importance of Geographic Information System (GIS) fusion methods with natural language processing models (NLP) for urban research, and identifies directions for further analysis, including accompanying problems and limitations in the present day. 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

Persistent deviations from Uncovered Interest Rate Parity (UIRP) represent a central puzzle in international finance and a key source of currency risk for global investors. This study examines the UIRP puzzle in the JPY/USD market through the lens of financial risk transmission, focusing on how risk premiums, liquidity conditions, and relative equity market performance jointly shape short-run exchange rate dynamics. Using daily data from 2018 to 2024, we employ a vector autoregression (VAR) framework to capture the endogenous interactions between change in the interest rate differentials, foreign exchange liquidity, global risk indicators (including the VIX, oil price shocks, and currency risk reversals), and relative equity returns consistent with the Uncovered Equity Parity (UEP) hypothesis. The results reveal that traditional interest rate differentials do not directly explain short-term exchange rate movements, confirming persistent UIRP deviations. Instead, risk-related financial channels act as indirect financial risk transmission channels. Shocks to global risk sentiment and currency risk premiums significantly affect JPY/USD returns, while relative equity market performance emerges as a key intermediary linking risk conditions to exchange rate adjustments. The findings also support the Japanese Yen’s continued role as a safe-haven currency during periods of heightened market uncertainty and underline the importance of carry trade dynamics in amplifying risk-driven exchange rate fluctuations. Overall, this study highlights the importance of integrating financial risk measures and portfolio-based transmission channels into exchange rate models. The results have direct implications for risk management, currency exposure hedging, and the assessment of systemic risk spillovers across financial markets. Full article

Increasing community awareness of, and engagement in, biodiversity and nature are key elements in many environmental conservation strategies. However, the public may take little or no action to protect biodiversity even though they may feel a strong sense of concern about its decline. This suggests that, although members of the public may be cognitively and affectively engaged with conserving biodiversity, this engagement does not necessarily translate into behavioural engagement and support for environmental policies. We hypothesised that the association between cognitive and affective engagement with conserving biodiversity on the one hand, and conservation behaviour on the other, depends on the relevance and importance of conserving biodiversity with respect to personal needs. Using a survey of the New Zealand public (n = 1000) we found that engagement with biodiversity was associated with the personal relevance and needs-based importance of conserving biodiversity. Importantly, using conditional process analysis, we found that involvement moderates the link between cognitive and affective engagement and conservation behaviour with the link strengthening as involvement intensifies. These findings help to explain why cognitive and affective engagement with conserving biodiversity do not translate inevitably into behavioural engagement with conserving biodiversity and support for environmental policies. The implication is that, to stimulate action, knowledge and sentiment must be accompanied by the perception that action to protect biodiversity will contribute in significant ways to meeting personal needs. Full article

The article examines how framing and actor identity structure attention in short-video politics using a country-level corpus from Ecuador. It assembles 4612 public TikTok videos from official accounts and politically salient hashtags, extracts multimodal text via automatic speech recognition and on-screen OCR, and constructs two continuous indices: a quality index (programmatic, efficacy-oriented content) and a populism index (antagonistic, people-versus-elite cues). Engagement is modeled as a fractional response (binomial GLM with logit link), with robustness checks using OLS on logit(ER) and Poisson counts with an offset for log(plays + 1). Models include affect (positive sentiment and anger), hour/day controls, and actor fixed effects (leader, creator, institution, party, and media). The indices display construct validity: quality aligns with positive/joyful tone and populism with anger. Net of controls, populism is positively and consistently associated with engagement across estimators; quality is small and often null or negative. Effects are heterogeneous: leaders gain under both frames, creators primarily under populism, and media modestly under populism, while institutions face penalties under both, and parties show limited returns. Monthly series reveal event-linked intensification of populism, and hashtag networks are modular, mapping onto institutional, partisan, and creator ecosystems. A design analysis identifies a non-populist pathway—benefit-first micro-explanations, concise captions, targeted hashtags, and joyful/efficacy affect—that raises engagement without antagonism. The study contributes a reproducible, open-source pipeline for survey-free, multimodal framing measurement and clarifies how persona × frame interactions and meso-level discursive structure jointly organize attention in short-video politics. Full article

Hyperparameter optimization for cryptocurrency trading strategies encounters distinct challenges owing to continuous operation, volatility rates 3–4 times higher than equity indices, and price dynamics influenced by market sentiment. Bayesian optimization (Tree-Structured Parzen Estimator, TPE) and evolutionary algorithms (Differential Evolution, DE) are great for machine learning, but there are not many systematic comparisons for trading cryptocurrencies. This research evaluates Random Sampling, TPE, and DE through 36 factorial experiments, comprising 3 trading strategies (3, 4, and 5 hyperparameters) × 3 optimizers × 4 cryptocurrency pairs (BTC/USDT, ETH/USDT, INJ/USDT, SOL/USDT), resulting in 14,400 backtesting trials with walk-forward validation. TPE won 75% of strategy–asset pairs (9 of 12), reaching 90% of optimal performance within 13–17% of trial budgets. We find strategy-specific optimizer compatibility: mean-reversion strategies show DE underperformance independent of topology (−1% to −8%), whereas trend-following strategies show consistent DE competitiveness across assets (+13% to +37%). Most notably, for the same strategy, parameter space topology differs significantly between assets (trend following: 4.6% viable on BTC to 82% on ETH = 17.8×; mean reversion: 10.8% on ETH to 92% on SOL = 8.5×), indicating that topology results from strategy–asset interaction rather than intrinsic properties. Complete testing failures and widespread severe overfitting point to regime non-stationarity as a fundamental problem. Among the contributions are: (1) evidence shows that topological effects are dominated by optimizer–strategy compatibility (DE fails on mean-reversion strategies even in 92% viable spaces, but succeeds on trend-following strategies regardless of topology, spanning 13.6–82% viable spaces); (2) this is the first systematic Bayesian versus evolutionary comparison across 4 cryptocurrency assets; (3) parameter space topology emerges from strategy–asset interaction, varying up to 17.8-fold; and (4) single-period backtests inadequately identify parameter instability. Full article

This article examines how the Global Innovation Index (GII) has become the dominant technical language for assessing and legitimizing countries’ innovation performance, and what this implies for middle-income economies such as Ecuador and Peru. We conduct a systematic review of 89 Scopus-indexed studies, combining bibliometrics with natural language processing of abstracts. The results reveal a largely optimistic discourse that frames innovation as a national, systemic construct—structured around institutions, human capital, infrastructure, market and business sophistication—while relying heavily on standardized GII metrics. Topic modeling and sentiment analysis show limited critical scrutiny of the index itself. The comparative analysis of Ecuador and Peru highlights persistent gaps between innovation inputs and outputs, with Peru leading in human capital and markets but lagging in business sophistication, and Ecuador constrained by institutional and market weaknesses. We argue that the GII should be used as a diagnostic and reform tool, not merely as a reputational ranking. Full article

Background/Objectives: The prominent role the internet plays in being a source of dental information prompts qualitative evaluation of relevant online content. This study aimed to explore patients’ experience regarding periodontal graft surgery communicated through the social media platform YouTube. Methods: An initial YouTube search using the term “gum surgery experience” retrieved 40 videos. Graft surgery was the most frequently discussed procedure, and 19 relevant videos were included in the qualitative analysis. Video content was analysed using a combined human-centered and artificial intelligence (AI)–assisted approach. AI-supported analysis of viewer comments was conducted using ChatGPT-4 and Gemini-1.5 Pro. Themes generated by human and AI analyses were compared. Results: Nine key themes were identified from the 19 videos that satisfied selection criteria. Most themes were similar between human and AI analyses, with six overlapping and three unique. The most frequently coded theme was post-operative recovery (n = 177), with pain, work absence, eating difficulties, and disrupted oral hygiene commonly reported. Patient-clinician relationships were frequently highlighted, with mixed experiences regarding communication and trust. Positive experiences were reported more frequently than negative. Comment analysis revealed varied audience engagement and sentiments, emphasizing concerns about pain, recovery, and procedural anxiety. Conclusions: Key themes related to patient experiences were identified, notably concerns regarding post-operative recovery and patient-clinician relationships. Challenges in finding information prior to having surgeries motivated patients to provide support and advice on YouTube, emphasizing the need for patient-centered resources and effective patient-clinician communication. Integrating human and AI methods in qualitative analysis was efficient and insightful, with AI supplementing but not substituting human research. Full article

The transition to environmentally sustainable healthcare is gaining urgency, yet public discourse shaping this shift remains underexamined. This study employs natural language processing (NLP) to analyze 15,976 English-language tweets (2006–2024) related to sustainable healthcare. Using Latent Dirichlet Allocation (LDA), eight dominant topics were identified, including eco-friendly access, net-zero implementation, climate impact, emissions, cost and waste, education, infrastructure, and green technologies. Sentiment analysis (VADER) of 9433 tweets showed 59.1% positive, 31.1% neutral, and 9.8% negative sentiment, with AI and technology topics receiving the highest positivity (73.5%) and climate-related topics the most negativity. Thematic analysis of 800 tweets revealed six cross-cutting themes, including healthcare’s environmental responsibility, co-benefits for health, urgency of climate action, and optimism in technological solutions. These findings offer a nuanced understanding of public perceptions, informing targeted strategies and communication for healthcare sustainability. The study also demonstrates the value of mixed-method NLP in examining enablers and barriers to health system transformation. Full article

This paper presents a comprehensive empirical evaluation comparing meta-model aggregation strategies with traditional ensemble methods and standalone models for sentiment analysis in recommender systems beyond standalone large language model (LLM) performance. We investigate whether aggregating multiple LLMs through a reasoning-based meta-model provides measurable performance advantages over individual models and standard statistical aggregation approaches in zero-shot sentiment classification. Using a balanced dataset of 5000 verified Amazon purchase reviews (1000 reviews per rating category from 1 to 5 stars, sampled via two-stage stratified sampling across five product categories), we evaluate 12 different leading pre-trained LLMs from four major providers (OpenAI, Anthropic, Google, and DeepSeek) in both standalone and meta-model configurations. Our experimental design systematically compares individual model performance against GPT-based meta-model aggregation and traditional ensemble baselines (majority voting, mean aggregation). Results show statistically significant improvements (McNemar’s test, p < 0.001): the GPT-5 meta-model achieves 71.40% accuracy (10.15 percentage point improvement over the 61.25% individual model average), while the GPT-5 mini meta-model reaches 70.32% (9.07 percentage point improvement). These observed improvements surpass traditional ensemble methods (majority voting: 62.64%; mean aggregation: 62.96%), suggesting potential value in meta-model aggregation for sentiment analysis tasks. Our analysis reveals empirical patterns including neutral sentiment classification challenges (3-star ratings show 64.83% failure rates across models), model influence hierarchies, and cost-accuracy trade-offs ($130.45 aggregation cost vs. $0.24–$43.97 for individual models per 5000 predictions). This work provides evidence-based insights into the comparative effectiveness of LLM aggregation strategies in recommender systems, demonstrating that meta-model aggregation with natural language reasoning capabilities achieves measurable performance gains beyond statistical aggregation alone. Full article