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 …

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 …

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of
symptoms and signs that are persistent, exacerbated or newly incident in the period after …

Topic modeling is a well-established technique for exploring text corpora. Conventional
topic models (eg, LDA) represent topics as bags of words that often require" reading the tea …

Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication
research. Yet, questions regarding reliability and validity of the approach have received little …

Analyzing short texts infers discriminative and coherent latent topics that is a critical and
fundamental task since many real-world applications require semantic understanding of …

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 …

Topic models are one of the most popular methods for learning representations of text, but a
major challenge is that any change to the topic model requires mathematically deriving a …

Quantifying the coherence of a set of statements is a long standing problem with many
potential applications that has attracted researchers from different sciences. The special …