BackgroundChildhood learning disabilities and neurodevelopmental disorders have been increasingly linked to early-life environmental chemical exposures, including air pollutants, heavy metals, endocrine-disrupting chemicals, and pesticides. Despite growing academic interest, a comprehensive analysis of global research trends and emerging themes in this interdisciplinary field remains lacking.MethodsA bibliometric analysis was conducted using the Web of Science Core Collection (WoSCC) database to identify studies published between January 2005 and December 2025. Articles and reviews written in English focusing on environmental exposure and childhood learning disabilities were included. CiteSpace software was employed to analyze annual publication trends, country and institutional contributions, co-authorship networks, co-cited references, and keyword clustering and evolution. In addition, we applied Latent Dirichlet Allocation (LDA) topic modeling to abstracts/keywords to uncover latent thematic structures and quantify topic prevalence across the corpus.ResultsA total of 1056 publications were included. Global research output increased steadily, with a notable surge after 2017. The United States led in publication volume and international collaboration, followed by China, the United Kingdom, and Spain. Influential institutions included Harvard University, Columbia University, and ISGlobal. Key authors such as Jordi Sunyer, David Bellinger, and Brenda Eskenazi were identified as central contributors. Frequently co-cited journals included Environmental Health Perspectives and Environmental Research. Major research clusters focused on air pollution, endocrine disruptors, oxidative stress, and neurodevelopmental disorders. Timeline and burst analyses revealed a shift from traditional toxicants (e.g., lead, mercury) to complex outcomes such as academic performance and mental health, with growing attention to mechanisms like epigenetics and environmental justice. LDA topic modeling revealed 15 themes spanning exposure settings (indoor/residential/air pollution), neurodevelopmental outcomes (autism/ADHD/cognition), and key neurotoxicants (pesticides/PCB, arsenic, methylmercury), suggesting an evolving focus toward functional outcomes and mechanisms.ConclusionsThis study highlights the evolving landscape of research linking environmental exposures to childhood cognitive and behavioral outcomes. The field is expanding from exposure identification to mechanistic understanding and real-world functional implications. Greater interdisciplinary collaboration and equity-focused research are needed to inform policy and protect child brain health globally.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41182-026-00949-z.

Sporotrichosis, a chronic infectious disease caused by the Sporothrix schenckii complex, has seen a rising incidence globally due to environmental changes and an increasing population of immunocompromised individuals, presenting a significant public health challenge. This study aims to elucidate the primary research themes, influential authors and institutions, and the evolution of sporotrichosis research trends through bibliometric analysis. In addition, we employ Latent Dirichlet Allocation (LDA) for topic modeling to identify potential research avenues and conceptual associations, while text mining analysis is used to uncover research gaps. A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science Core Collection (1975-2024), yielding 1299 records. Various analytical tools, including Citespace, VOSviewer, and R's bibliometrix package, were used for data analysis and visualization. The LDA algorithm was applied for topic modeling. The analysis revealed an increasing trend in publications and citations, with Brazil leading in research output. Key authors and journals were identified, highlighting a shift in research focus from epidemiology and clinical manifestations to treatment methods, drug development, and pathogen classification. Topic modeling identified five major research directions. This bibliometric analysis highlights the interdisciplinary nature of sporotrichosis research and identifies critical areas for future investigation, including pathogenesis, diagnostic improvements, and novel therapeutic strategies. Enhanced international collaboration and resource sharing are essential to address the challenges posed by sporotrichosis, ultimately improving public health outcomes related to this disease.

Healthcare services constitute a crucial aspect in improving public well-being. Every individual has the right to receive healthcare services that are of high quality, safe, efficient, and affordable. This study aims to identify and analyze public perceptions and sentiments toward healthcare services at RSUD Soe, as well as to evaluate the performance of several machine learning methods in classifying such sentiments. The data were collected from 278 respondents through a Likert-scale questionnaire that represents perceptions and levels of satisfaction regarding various service aspects. Sentiment analysis was conducted using four machine learning algorithms, namely Naïve Bayes, C4.5, Random Forest, and Support Vector Machine. The results indicate that Naïve Bayes achieved the highest accuracy of 82.14 percent, followed by SVM at 80 percent, Random Forest at 79 percent, and C4.5 at 73.21 percent. This study also applied the Latent Dirichlet Allocation method to identify the main themes within public feedback. LDA generated twelve topics reflecting key issues such as waiting time, availability of medical personnel, facility cleanliness, and the attitudes of healthcare staff. The majority of comments exhibited positive sentiment, particularly concerning staff friendliness and service quality. These findings were used to formulate improvement recommendations, including enhancing service quality, increasing the number of medical personnel, and optimizing facilities. This research demonstrates that a data-driven quantitative approach is effective in evaluating healthcare service quality and supporting more targeted decision-making. The results are expected to assist RSUD Soe in continuously and effectively improving service quality.

Virtual reality (VR) has been increasingly adopted as a digital tool in rehabilitation for balance training, coordination improvement, and motor recovery, yet the literature remains dispersed across clinical rehabilitation, exercise-based interventions, and broader motor-related applications. This fragmentation makes it difficult to determine how the field has evolved and where research emphasis has shifted. This study mapped the research landscape and thematic evolution of VR for balance, coordination, and motor rehabilitation using bibliometric analysis and topic modeling. A total of 1258 articles indexed in the Web of Science Core Collection from 2011 to 2025 were analyzed. Only English language articles and reviews relevant to VR-based balance, coordination, or motor rehabilitation research were included, yielding a final dataset of 1258 publications. CiteSpace and VOSviewer were used to examine keyword co-occurrence, clustering patterns, and temporal trends, while Latent Dirichlet Allocation (LDA) was applied to identify latent themes and their temporal dynamics. The field has moved beyond early feasibility testing toward a more differentiated landscape shaped by distinct clinical targets, population groups, and training purposes. Seven recurring themes were identified, including vestibular rehabilitation and immersive training, post-stroke upper-limb rehabilitation, efficacy and adverse-effect assessment, balance and gait training interventions, evidence synthesis and review-based evaluation, elderly exercise and cognitive interventions, and skill-oriented virtual task training with recent expansion toward broader population groups and task-specific applications beyond traditional rehabilitation settings. VR research on balance, coordination, and motor rehabilitation has evolved into a more thematically differentiated field rather than remaining a single rehabilitation-oriented domain. By combining bibliometric mapping with topic modeling, this study clarifies where evidence is concentrated and which thematic directions are gaining visibility, providing a clearer basis for future evidence synthesis and more comparable intervention reporting.

In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult to accurately translate user emotions into specific design solutions. To address this challenge, this study proposes an integrated Kansei Engineering–machine learning framework for optimizing product design. First, user perceptual data are collected through questionnaires and interviews, and key perceptual imagery words are extracted using the Latent Dirichlet Allocation (LDA) model and factor analysis. Then, product design elements are systematically decomposed, and their relative importance is determined using the fuzzy analytic hierarchy process (FAHP). Based on this, a mapping relationship between perceptual imagery and design elements is established. Subsequently, the XGBoost model is employed to predict and optimize design element combinations. The optimized design schemes are further generated using AIGC technology and validated through eye-tracking experiments and subjective evaluations.The results show that the proposed method achieves high predictive accuracy (R² = 0.87) and significantly improves the emotional expression of product design. This study contributes to the integration of Kansei Engineering and machine learning by providing a data-driven approach for emotional design optimization, offering theoretical, practical, and strategic guidance for intelligent product design in industrial contexts.

This article introduces the CoLiRa (Computational Literature Review & Analysis) framework, a novel integration of established computational algorithms designed to quantitatively analyze and map the evolution of scientific fields. Employing a human-in-the-loop epistemological approach, CoLiRa combines the scalability of automated algorithms with the semantic coherence of expert-driven qualitative research. The multi-stage pipeline incorporates Latent Dirichlet Allocation (LDA) for thematic discovery, cluster analysis (K-Means and Multidimensional Scaling) for conceptual mapping, and Ordinary Least Squares (OLS) regression to monitor temporal trends. Algorithmic outputs are structurally validated by domain experts using quantitative metrics. The framework’s end-to-end capabilities are demonstrated through a proof-of-concept case study on the semantic enrichment of tabular data, encompassing studies up to 2024 that utilize Semantic Web ontologies, Linked Data, and knowledge graphs. The analysis identifies three core research topics and finds no statistically significant linear trends, suggesting thematic coexistence. This work provides a validated, hybrid computational approach for conducting robust literature reviews and mapping research trajectories.

The rapid growth of scientific output in institutional repositories has created significant challenges for the efficient retrieval of information, particularly when searches rely solely on unstructured metadata. Although topic modelling has been widely applied to large bodies of text, little attention has been given to Indonesian-language repositories and metadata-only datasets harvested through standardized protocols. This study aims to address this issue by using Latent Dirichlet Allocation (LDA) to analyze the research landscape of the Widyatama University Repository, based on titles and abstracts that were collected automatically via the OAI-PMH protocol. The proposed methodology integrates the following processes: automated metadata harvesting; Indonesian-language text preprocessing; probabilistic topic modelling; and quantitative evaluation using coherence metrics, complemented by qualitative interpretability analysis. The experimental results show that the optimal model was achieved with 12 topics, giving a Coherence Score of 0.5546 categorized as 'Good'. This demonstrates that meaningful thematic structures can be extracted even from limited textual metadata. The identified topics reflect the university's main research areas, such as Marketing Management (12.5%), Auditing (12.4%), and Human Resource Management (12.1%), as well as specific domains like Informatics (6.7%). To enhance practical usability, the model outputs were deployed in an interactive, Streamlit-based dashboard enabling dynamic exploration of topic relationships and temporal trends. This study contributes to repository analytics by demonstrating how topic modelling driven by metadata can transform institutional repositories into intelligent systems for discovering knowledge, supporting the navigation of research, landscape analysis and evidence-based decision-making for academic management.

Public opinion, which may be influenced by personal experiences, news, and social media, can impact compliance with public health measures (PHMs) during health emergencies. Artificial intelligence (AI) tools offer opportunities to analyze public opinion in real time during health emergencies. However, their performance in accurately identifying sentiment and themes in health-related online content remains unclear. This study aimed to evaluate the performance of natural language processing-based and large language model (LLM)-based AI tools when compared to human coding for sentiment analysis, topic modeling, and thematic analysis of public health datasets. Tools were selected to reflect those available to public health analysts and decision-makers. Data were collected via Google Alerts (GA) and social media posts from X (formerly known as Twitter) relevant to COVID-19 mitigation PHMs from December 2022 to February 2023. Following relevance screening, the sentiment of the complete datasets was analyzed by a human rater, with descriptive statistics used to summarize the overall sentiment profile. Subsets of 400 GA articles and 400 tweets were manually coded for sentiment by 2 human raters. Results were compared with outputs from 5 AI tools, including VADER (Valence Aware Dictionary and Sentiment Reasoner), SentimentGI, SentimentQDAP, Microsoft Azure, and OpenAI's ChatGPT-4. Topic modeling of the GA and X datasets was conducted using latent Dirichlet allocation in R and zero-shot prompting in ChatGPT-4 and compared with manual topic summaries. Thematic analysis of positive and negative sentiment datasets was conducted by a human rater and ChatGPT-4, with outputs evaluated for proficiency and reasonableness. The sentiment of the entire datasets was analyzed by a human rater, and descriptive statistics were calculated. Of 2227 GA results and 3484 tweets, 58% (n=1238) and 71% (n=2473), respectively, were relevant to PHMs. Human-coded sentiment analysis showed mostly neutral reporting in the news media, while social media expressed more polarized views. Across both datasets, AI tools demonstrated poor concordance with human-coded sentiment (Cohen κ <0.5 for all tools and sentiment categories). Topic modeling with ChatGPT-4 aligned more closely with human-rated topics than latent Dirichlet allocation, and of the 20 LLM-generated thematic outputs, 13 were rated proficient, and 7 were rated partially proficient. LLM outputs provided coherent, high-level summaries but lacked contextual insight. Human and LLM thematic analyses both identified themes of vaccine effectiveness, debate regarding PHMs, and public trust. Accessible AI tools demonstrate limited reliability for sentiment classification of health-related online text but show promise for rapid thematic exploration when combined with human oversight. These tools could complement traditional qualitative research in the context of health emergencies; however, they require human review to enhance the accuracy of interpretation. Further research is needed for non-English datasets.

ABSTRACT Accurately forecasting carbon emission allowance (CEA) prices is essential for supporting China’s low-carbon transition, yet the national carbon market remains volatile and highly sensitive to policy signals. This study develops a prediction framework that incorporates topic information and sentiment measures by integrating unstructured news reports with historical price data. We first identify thematic structures in carbon-related news using four large language models (LLMs), which cover two categories emphasizing deep reasoning capability and rapid response, respectively, along with Latent Dirichlet Allocation (LDA), and then extract topic-specific sentiment using the same LLMs and a traditional lexicon-based sentiment approach. These sentiment indicators are incorporated into a long short term memory (LSTM) forecasting model, and structural break analysis is employed to capture regime shifts in market dynamics. Empirical results show that sentiment augmented models consistently outperform price-only benchmarks. SHapley Additive exPlanations (SHAP) analysis further indicates that discourse related to market trading contributes substantially to predictive improvement.

Abstract. The destruction of the Kahovka Dam in June 2023 caused one of the largest ecological disasters in contemporary Europe and triggered profound hydrological, geomorphological, and biotic transformations across the Lower Dnipro region. This catastrophe created an urgent need for practical ecological research and for broader theoretical generalization within the field of catastrophic ecology. The objective of this study was to determine how catastrophic ecosystem change is structured in the international scientific literature and to clarify the conceptual position of the Kahovka catastrophe within this broader research landscape. A bibliographic corpus of publications on catastrophic changes in ecosystems was analyzed using text mining of titles, abstracts, and keywords. After preprocessing and filtering of lexical units, Latent Dirichlet Allocation was applied to identify the main thematic blocks of the literature. Their relationships were examined using Multidimensional Scaling, while conceptual structuring was assessed through citation analysis using generalized linear modeling and topic-specific distances from thematic centroids. The analysis revealed five major thematic directions: catastrophic shifts of ecosystems, climate change as a cause of ecosystem catastrophes, habitat extinction in the context of ecosystem catastrophes, catastrophic disturbance and forest ecosystem reorganization, and catastrophic change in aquatic and riparian ecosystems. The multidimensional configuration showed that catastrophic ecology is organized as a differentiated semantic field rather than as a single homogeneous discourse. The climate-change and aquatic-riparian blocks demonstrated the strongest increase in prominence over time, whereas the disturbance block showed a gradual decline. At the same time, the catastrophic-shift and disturbance themes displayed clearer conceptual structuring, because citation-effective publications were concentrated closer to their thematic centroids. These findings indicate that catastrophic ecology combines a central theoretical discourse on resilience, thresholds, and regime shifts with several partially autonomous applied domains related to climate, biodiversity loss, disturbance, and water-related transformations. In this perspective, the Kahovka catastrophe can be understood as a contemporary large-scale case that connects several of these semantic directions simultaneously and therefore provides an important basis for the further development of catastrophic ecology as an empirical and theoretical field.

Abstract This study develops an explainable machine learning model to predict cryptocurrency delistings using Binance data. It combines quantitative indicators (price, volume) with qualitative data from real‐time news and Reddit. Latent Dirichlet Allocation (LDA) is used to extract topic trends and community reactions, which are transformed into time‐series features. XGBoost, LightGBM, and CatBoost are compared, with SHAP applied for model interpretability. Results show that sharp price drops, repeated risk‐topic exposure, and Reddit responses strongly predict delisting. XGBoost achieves the best performance, offering practical insights for early warning systems and investor protection.

Topic modeling is a process that discovers key themes in unstructured text data by identifying the distribution of topics and words in a document, revealing hidden dimensions. Latent Dirichlet allocation is a widely used generative probabilistic topic model, but it cannot capture the dependency between topics. Generally, the topics within a document are primarily influenced by its overarching theme which naturally interrelates the topics. Thus, it is imperative to unveil such relationships between the topics. To this end, this study proposes a multilevel topic model (MTM) to unearth the hidden topic dependency in a corpus through multilevel latent structure. The MTM allows word-based topic proportions to vary across the higher-level latent structure. The parameters are estimated with a modified EM algorithm using an upward-downward approach to alleviate the computational complexity. Empirical studies on corpora have also been conducted on the multilevel topic model and the hierarchy of multilevel topic model have been interpreted. These analyses have demonstrated that the proposed multilevel topic model outperforms latent Dirichlet allocation in terms of systematic interpretability.

Due to the inherent subjectivity of Kansei perception, aligning the front-end styling of new energy vehicles (NEVs) with users' emotional preferences remains a complex challenge. This study proposes a data-driven framework integrating semantic mining and deep learning to quantify and optimize such emotional responses. A Latent Dirichlet Allocation (LDA) model was employed to extract four core emotional dimensions-fashion, power, technology, and sportiness-from a corpus of user-generated content (UGC). To establish a mapping between abstract emotions and concrete morphological features, rough set theory (RST) was applied for dimensionality reduction, retaining only the most influential design attributes. In addition, an attention-enhanced long short-term memory (LSTM) network optimized via a genetic algorithm (GA) was constructed to predict emotional evaluations. This hybrid model enables targeted design configuration generation for NEV front-end styling based on specific emotional indicators. The results demonstrate that the proposed approach effectively bridges the gap between qualitative user imagery and quantitative design features, providing the automotive industry with a robust emotion-oriented design support tool.

The paper focuses on the persistent conflict between Israel and Palestine. of world discussion, with such sites as Reddit containing a great deal medium for public expression. This study uses natural language processing (NLP) including valence aware dictionary and sentiment reasoner (VADER) for sentimental analysis and latent Dirichlet allocation (LDA) for topic modelling of geopolitical positions in comments of 400,000 posts belonging to the topics subreddits. A sentiment analysis using VADER shows that negative sentiments observed and indicate that such sentiments prevail throughout fear of aggression, human suffering, and threat. media biases. LDA analysis for topic modelling. The process of LDA reveals such important topics as humanitarian ones. media discourses, polemizing and appeals for calm. Furthermore, the study which divides comments into geopolitical attitudes – support for or yet, Israelis, supporters of Palestine, opponents of both, and neutrals, offering a contingency comprehensive conception of public perspectives. The results emphasize the effectiveness of large-scale. Of the big data pre-processing techniques, NLP can be used to understand geopolitics and its intricate dynamics. educate policy makers, researchers, and media planners.

Abstract Urban environmental resilience is an important strategic direction for environmental governance. In China, it exhibits distinctive characteristics shaped by its government-led governance model, rapid economic transformation, geographical diversity, and high exposure to climatic risks. Therefore, studying its excellent and not excellent cases can help provide a unique perspective and thinking for the development of urban environmental resilience. To clarify its key elements and combination pathways, the study first employed the Latent Dirichlet Allocation (LDA) model to identify nine key topics of urban environmental resilience construction. Based on this, an ‘Attention-Resource-Governance’ (ARG) theoretical model was constructed to describe the operational mechanism of urban environmental resilience building. Subsequently, fuzzy-set Qualitative Comparative Analysis (fsQCA) was used to analyze the configuration pathways among 37 cities in South China. The results reveal four types of high-resilience pathways—such as technology-driven synergy and resource-led governance—and three types of non-high-resilience pathways, highlighting the coexistence of multiple causal mechanisms. For instance, cities like Shenzhen and Dongguan demonstrate high-level resilience through technological and financial integration, whereas Guilin and Sanya show an ‘inverted pattern’ of high ecological value but low resilience. Overall, high-level urban environmental resilience requires synergy among ‘attention-resources-governance’ dimensions.

ABSTRACT Opinion summarization aims to refine opinions from large‐scale reviews, often using select‐then‐summary methods. Due to the length limitation of the input, only a small number of samples are usually selected for the summarization model, with the risk of ignoring global opinion information such as product aspects and user sentiments. Topic modeling can unsupervisedly extract topic words from texts, holding the potential for capturing global opinion. Therefore, we propose Depictor , a topic‐guided two‐stage opinion summarization approach with dual‐perspective topic modeling (BERTopic and Latent Dirichlet allocation). The dual‐perspective topic modeling extracts topic words from both semantic and statistical perspectives. Then the extracted topic information is incorporated into the generator in two ways. For the input side, the topic words are concatenated with the subset reviews from the extractor as supplementary keyword information. For the representation side, an additional topic‐driven attention mechanism focusing on the topic words is added to enable the summarization model to pay extra attention to aspect‐related keywords during the generation. Experimental results on AmaSum show that the proposed topic‐augmented method outperforms several strong baselines, indicating its effectiveness in opinion summarization.

Social media sentiment exposure to climate-induced floods and risk responses to the 2022 flood in Pakistan.

A 'Cluster-then-Estimate' Natural Language Processing (NLP) Approach for Classifying Maritime Incident Severity Based on Textual Descriptions.

Social media insights into patient perceptions of orthognathic surgery.

This study investigates how narratives about South African virtual influencer Kim Zulu in media coverage position fashion as a part of cultural tourism marketing. A qualitative research design and an interpretive worldview were adopted to analyse media articles published between 2020 and 2025. The analysis was conducted with Voyant Tools for topic modelling using the Latent Dirichlet Allocation (LDA) algorithm, cluster analysis and interpretive reading of the corpus supported by three theoretical lenses, namely cultural tourism, Destination Image Theory and Afrofuturism. The results indicate that fashion is not presented in isolation when reporting on Kim Zulu in media coverage. Rather fashion links to culture, futurity, and destination branding that strengthens South Africa’s visibility and appeal in the global tourism market. The paper adds to the literature on cultural tourism marketing, digital culture, and virtual influencer marketing while adding to research on digital cultural tourism in the Global South.