Samantha Herron, a 2017 Junior Fellow at the Library of Congress, has written a post on The Signal detailing common forms of textual analysis. Herron describes techniques such as stylometry, “the practice of using linguistic study to attribute authorship to an anonymous text,” and topic modeling.
Her summary also includes examples of tools like Voyant, used for determining word frequencies:
Computers can count up and rank which words appear most often in a text or set of texts. Though not computationally complicated, term frequency is often an interesting jumping off point for further analysis, and a useful introduction into some of digital humanities’ debates. Word frequency is the basis for somewhat more sophisticated analyses like topic modeling, sentiment analysis, and ngrams.
Herron’s post ends with a curated list of tools and tutorials for textual analysis.
dh+lib Review
This post was produced through a cooperation between Erica Hayes, Elise Daniel, Sarah Ames, Courtenay McLeland, Emily Esten, Kristen Mapes, Amber D'Ambrosio, and Heidi Winkler (Editors-at-large for the week), Sarah Melton (Editor for the week), and Nickoal Eichmann-Kalwara, Caitlin Christian-Lamb, Roxanne Shirazi, and Patrick Williams (dh+lib Review Editors).