Topic modeling is a unique approach to extract information and digest the results from a collection of documents. This article defines topic modeling as, understanding “buckets of words,” and providing seductive but obscure results in the forms of easily interpreted (and manipulated) “topics.” There remain many tools within the field of topic modeling, and specifically, in new tools under the category of machine learning. From the article, we note, “The work in this issue integrates the Natural Language Processing technique of topic modeling with network representation, GIS, and information visualization. This approach takes advantage of the growing accessibility of tools and methods that had until recently required great resources (technical, professional, and financial).” With new and more accessible tools, we expect topic modeling to be used more widespread.
A main concern of topic modeling is that is the usefulness and easy of use for topic modeling tools. In this article, “none of the authors in this issue simply run and accept the results as “useful” or “interesting” for humanities scholarship. Instead, they critically wrestle with the process. Their work is done with as much of a focus on what the computational techniques obscure as reveal.” The next steps for topic modeling will be to ensure that we can reveal useful information that is conclusive for the process of analysis, with far more accuracy and efficiency than we see in hand-process techniques.