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 …

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 …

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 …

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 …

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 …

accepted on 03 July 2019, published on 24 July 2019 Copyright© 2019 Pooja Kherwa et al.,
licensed to EAI. This is an open access article distributed under the terms of the Creative …

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 models have been prevalent for decades to discover latent topics and infer topic
proportions of documents in an unsupervised fashion. They have been widely used in …

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 …