Text preprocessing is not only an essential step to prepare the corpus for modeling but also
a key area that directly affects the natural language processing (NLP) application results. For …

In recent years, fully automated content analysis based on probabilistic topic models has
become popular among social scientists because of their scalability. However, researchers …

Matching for causal inference is a well-studied problem, but standard methods fail when the
units to match are text documents: the high-dimensional and rich nature of the data renders …

One of the most widely used approaches in natural language processing and information
retrieval is the so-called bag-of-words model. A common component of such methods is the …

Sociologists are discussing the need for more formal ways to extract meaning from digital
text archives. We focus attention on the seeded topic model, a semi-supervised extension to …

While the validation of statistical properties of topics models is well established, the
substantive meaning of categories uncovered is often less clear and their interpretation …

With the success of contextualized language models, much research explores what these
models really learn and in which cases they still fail. Most of this work focuses on specific …

Objective We sought to understand spatial-temporal factors and socioeconomic disparities
that shaped US residents' response to COVID-19 as it emerged. Methods We mined …

Exploring students' discourse in academic settings over time can provide valuable insight
into the evolution of learner engagement and participation in online learning. In this study …

With communications being shifted to online social networks (OSNs) as a result of travel and
social restrictions during COVID-19 pandemic, the need has arisen for discovering emerging …