Topic modeling is used in information retrieval to infer the hidden themes in a collection of
documents and thus provides an automatic means to organize, understand and summarize …

Topic models have been applied to everything from books to newspapers to social media
posts in an effort to identify the most prevalent themes of a text corpus. We provide an in …

Regulations to contain the spread of COVID-19 have affected corporations, institutions, and
individuals to a degree that most people have never seen before. Information systems …

We are not able to deal with a mammoth text corpus without summarizing them into a
relatively small subset. A computational tool is extremely needed to understand such a …

Topic modeling analyzes documents to learn meaningful patterns of words. However,
existing topic models fail to learn interpretable topics when working with large and heavy …

Large language models (LLMs) are capable of successfully performing many language
processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify …

This paper combines qualitative and quantitative content analysis to map and analyze the
“Incel” worldview shared by members of a misogynistic online community ideologically …

Topic modelling is the new revolution in text mining. It is a statistical technique for revealing
the underlying semantic structure in large collection of documents. After analysing …

This paper assesses topic coherence and human topic ranking of uncovered latent topics
from scientific publications when utilizing the topic model latent Dirichlet allocation (LDA) on …

Topic modelling has been a successful technique for text analysis for almost twenty years.
When topic modelling met deep neural networks, there emerged a new and increasingly …