Topica: fast, all-purpose topic modeling for Python topica is a fast topic-modeling library for Python with more than a dozen models, built for social scientists who want to move from text data to ...

Chemotherapy-induced nausea and vomiting (CINV) remains a distressing adverse effect that compromises patients’ quality of life and treatment adherence. Traditional assessment methods often fail to ...

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...

Neural topic models grounded in hyperspherical geometry have recently advanced text-based topic discovery by modeling document-topic relationships using von Mises-Fisher (vMF) components, yielding ...

Abstract: By modeling global word co-occurrence patterns, topic models aim to uncover the underlying semantic structure of a corpus. However, their effectiveness is often undermined in short texts due ...

So, you want to learn Python, huh? It’s a pretty popular language these days, used for all sorts of things like making websites, crunching data, and even AI. The good news is, you don’t need to spend ...

Topic modeling is no longer just a statistical trick used to cluster words into themes. In 2026, it has evolved into a strategic intelligence tool that blends classical probabilistic modeling with ...

Systematic analysis of interview data can provide important insights into how young people experience and interpret their emotions. Both human-led qualitative (e.g., thematic analysis) and ...

Recent research has highlighted the limitations of the categorical approach to mental disorders and has increasingly supported the development of a transdiagnostic perspective. This emerging approach ...

Volatility forecasting is a key component of modern finance, used in asset allocation, risk management, and options pricing. Investors and traders rely on precise volatility models to optimize ...

"- the first document belongs to 4th topic. \n", "- the second document belongs to 4th topic. \n", "- the third document belongs to 6th topic. \n", ...