Brett's article purposes to introduce and exemplify topic modelling tools. Categorized as a form of text mining, Brett points to topic modelling as a way of sourcing out patterns in a corpus. In order to describe how topic modelling works, Brett illustrates using this analogy: "imagine working through an article with a set of highlighters. As you read through the article, you use a different color for the key words of themes within the paper as you come across them. When you were done, you could copy out the words as grouped by the color you assigned them. That list of words is a topic, and each color represents a different topic." Brett lists the "ingredients" necessary to successfully use topic modelling: a large corpus, familiarity with that corpus, a tool designed for topic modelling, and the knowledge to understand your results. While not necessarily useful as evidence, Brett argues that topic modelling is a great discovery tool.

Hulle begins the introduction to this book by differentiating between writing as product and writing as process. The approach of genetic criticism emphasizes writing as a process that strives towards invention. Hulle argues that genetic criticism brings forth a way to enhance the author's awareness of the writing process rather than focusing on the "artificial results." By privileging the material evidence for the creative process, genetic criticism is linked to scholarly editing in the sense that both are concerned with representing texts. In this book, Hulle defines and applies genetic criticism to three modernist writers (Joyce, Proust, and Mann) in order to clarify and exemplify the applicability of the theory.

In this article, Aditi Muralidharan and Marti Hearst discuss the text analysis tool WordSeer. Muralidharan and Hearst claim that WordSeer fills a specific gap in text analysis tools because WordSeer was designed with the specifics of literary research questions in mind. This stands in opposition to the appropriation of general text analysis software to answer literary questions. Muralidharan and Hearst conceptualize WordSeer as a tool that encourages scholarly queries, provides tools for both distant and close reading, and works within the exploratory process of reading, interpreting, and understanding. In order to demonstrate this, Muralidharan and Hearst present a case study using Shakespeare’s corpus and the research question “what are some things that are his and some things that are hers?”

In this article, Bradley discusses the balance between the computer’s ability to solve complex, formal tasks and the humanities bias towards indeterminacy. Bradley argues that researchers in the humanities use computers for one of two distinct types of tasks: manipulating data in order to facilitate research or performing complex textual analysis. Bradley demonstrates how analog research techniques can be replicated and rejuvenated in digital practices. In conclusion, Bradley argues that the questions that remain are not whether there are digital environments able to facilitate traditional research practices but (a) whether scholars are willing to use them and (b) whether they are successful.

In this article, Jacques Savoy tested the efficiency and reliability of using LDA (Latent Dirichlet Analysis) as a means of author attribution. Savoy opens the article by discussing the basic components of author attributions: a frequent word data set and a distance measurement. Noting that there is a limited number of test corpora for authorship attribution, Savoy compiles and describes the two test corpora used in his study: selections from English language publication the Glasgow Herald and selections from Italian language publication La Stompa. Savoy conducts authorship attribution tests on these data sets using delta, chi-sqaure, Kullback-Leibler, and Naive Bayes calculations as well as the LDA. While LDA is generally used to categorize texts into topics, Savoy concludes that it can be useful for authorship attribution.

In this article, Roberts provides a history and critical exploration of quantitative text analysis methods. Roberts seeks to "provide long-needed structure on a wide spectrum of text analysis methodologies" - something that he sees as lacking or being misrepresented in current scholarship. The objective of Roberts' article is to aid researchers in selecting the appropriate text analysis method for their project. Roberts discusses such examples as contingency analysis, instrumental analysis, thematic analysis, semantic analysis, and network analysis. As way of conclusion, Roberts cautions against common scholarly pitfalls in conducting proper quantitative text analysis, such as the importance of context and understanding how to interpret the data.

In this article, Sinclair discuss the - still - fraught relationship between the humanities and computing. Sinclair argues that scholars still see the sciences and humanities as opposing paradigms and that in order to rectify this relationship we must begin to see the computer as a resource for all types of scholarship. Sinclair encourages researchers to see computers as facilitating reading, exploration, and play when it comes to text analysis. He concedes that many of the current text analysis tools do little to convince skeptics that "good humanities research can be done with a computer." Sinclair introduces HyperPo as a model computational tool and as a case study to demonstrate how a tool can facilitate meaningful textual criticism. To conclude, Sinclair encourage scholars to explore texts with a bias towards serendipity - especially in using computational techniques.

In this blog post, Clay Templeton discusses topic modelling as a tool of "hermeneutic empowerment" in as far as the technique allows researchers to draw out the hidden structure of a corpus. Templeton's piece maps out the "genealogy of topic modelling in the humanities" by discussing key features and research initiatives related to this type of scholarship. Templeton acknowledges that topic modelling is often associated with "distant reading" because it isn't about the content of individual documents but rather about sourcing out patters among a group of documents. Templeton concludes his blog post by pointing outwards to scholarship that has successfully utilized topic modelling.

In this highly technical article written by computer science scholars, Blei, Ng, and Jordan discuss topic modelling textual corpora through LDA (latent Dirichlet allocation). This article begins by defining LDA and then moves through the steps necessary to carry out this type of analysis. Blei, Ng, and Jordan help their audience understand the common problems with LDA and purpose troubleshooting solutions. The article concludes with an exemplar analysis, complete with illustrative figures.