17 papers · ranked by Valyu relevance
Akramov, Roman, Khamatullin, Artem + 6 more
Choosing the number of topics T in Latent Dirichlet Allocation (LDA) is a key design decision that strongly affects both the statistical fit and interpretability of topic models. In this work, we formulate the selection of T as a discrete black-box optimization problem, where each function evaluation corresponds to…
Rebecca Danning, Zheng Tracy Ke, Rong Ma, Xihong Lin
Count data are ubiquitous across many applications in which understanding hidden patterns, or latent structure, is of interest. Topic modeling is a powerful tool for detecting latent structure in count data. However, standard topic modeling methods are often constrained by their restrictive assumptions, susceptible to…
Weiqing He, Bojian Hou, Amy Zheng, Yanbo Feng + 8 more
Recent research has explored various text analysis techniques to understand the experiences of dementia caregivers. Among these methods, topic modeling stands out as a powerful statistical tool for uncovering latent, or hidden, themes within large datasets. While topic modeling is widely used across various data types…
Yating Tao, Qian Shen, Weiqiang (Albert) Jin
The rapid emergence of ChatGPT has sparked extensive academic discourse across multiple fields. This study focuses on such discourse within the social sciences by examining how scholars frame and evaluate ChatGPT through research article abstracts. Drawing on 1,227 SSCI-indexed abstracts published between 30 November…
Saranzaya Magsarjav, Melissa Humphries, Jonathan Tuke, Lewis Mitchell
Topic modelling in Natural Language Processing uncovers hidden topics in large, unlabelled text datasets. It is widely applied in fields such as information retrieval, content summarisation, and trend analysis across various disciplines. However, probabilistic topic models can produce different results when rerun due…
Hanjia Gao, Hanwen Ye, Qing Nie, Annie Qu
Dynamic topic modeling is widely used to analyze evolving trends in scientific literature, medical records, and social media. Traditional topic models represent each topic through a single probability vector on the multinomial simplex and implicitly couple word occurrence and repetition within one probabilistic…
Kohei Watanabe
Social scientists have long been analyzing topics or themes of documents to understand important issues. As large textual data became more accessible, thanks to online news, social media, and digital archives, many found topic models very useful because their unsupervised algorithms allow users to identify topics in…
Iqra Mehmood, Zoya Zahra, Sarah Iqbal, Ayman Qahmash + 3 more
Background: Electronic Health Records (EHRs) are a rich source of clinical information used for patient monitoring, disease progression analysis, and treatment outcome assessment. However, their large-scale, heterogeneity, and temporal characteristics make them difficult to analyze. Topic modeling has emerged as an…
Ashmitha Rajendran, Parthiv Haldipur, Sonali Arora, Kaustubh Grama + 8 more
The cerebellar rhombic lip generates cerebellar progenitors and neurons that ultimately differentiate to comprise over half of all neurons in the adult human brain. Standard clustering approaches often fragment or miss rhombic lip progenitor populations entirely due to their transient nature, small size, and rapid…
Giovanni Spitale, Julia Seinsche, Rosa M S Visscher, Andrea Schöpf-Lazzarino + 7 more
Background Chronic illness may cause a financial burden that affects patients, their caregivers, and families. While international research, mostly from the United States, has largely focused on cancer-related financial hardship, less is known about whether financial distress due to other chronic illnesses exists…
Xiao Yuan, Ádám Arany, András Formanek, Yves Moreau + 2 more
Longitudinal microbiome data are key to understanding the dynamics of microbial communities and their relationships with the host and environment. However, analysis of such data is challenging due to high dimensionality, compositionality, irregular sampling and temporal dependencies on external covariates. Existing…
Daniel Palacios, Terry R. Hill
NASA Johnson Space Center has collected more than 54,000 space hardware failure reports. Obtaining engineering processes trends or root cause analysis by manual inspection is impractical. Fortunately, novel data science tools in Machine Learning and Natural Language Processing (NLP) can be utilized to perform text…
Simge Karakaş Mısır
The present study investigates the semantic structure, dominant themes, and temporal evolution of research on giftedness in early childhood through a comparative topic modeling approach. A final analytic sample (n = 518) of peer-reviewed journal articles indexed in the Scopus and Web of Science databases was analyzed.…
Yan Jiang, Sihong Liu, Philip A. Fisher
Topic modeling in applied psychology increasingly spans two methodological traditions: probabilistic bag-of-words models and newer embedding-based approaches. Yet many evaluations of these methods rely on longer and cleaner benchmark corpora, leaving less guidance for short, open-ended survey responses. This paper…
Huixiang Ouyang, Ching Wan, Ronald Fischer, Peter Karl Jonason
The past decades have generated a substantial volume of psychological literature on threat. However, the absence of systematic cross-field synthesis has resulted in limited understanding of major research domains and relationships between different lines of threat research. We analyzed 51,903 psychological publications…
Gabriele Malagoli, Filippo Valle, Andreina Tirabassi, Annalisa Marsico + 3 more
Recent advances in single-cell biology enable the profiling of multiple molecular layers, such as the transcriptome, epigenome, and surface proteins, within a single cell. Tackling the complexity of these data from different perspectives allows researchers to get the most complete insights into the biological…
Hegang Chen, Yuyin Lu, Yifan Zhao, Zhiming Dai + 4 more
Single-cell RNA sequencing technologies have revolutionized our understanding of cellular heterogeneity, yet computational methods often struggle to balance performance with biological interpretability. Embedded topic models have been widely used for interpretable single-cell embedding learning. However, these models…