Google's PageRank method was developed to evaluate the importance of web-pages via their link structure. The mathematics of PageRank, however, are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It's even used for systems analysis of road networks, as well as biology, chemistry, neuroscience, and physics. We'll see the mathematics and ideas that unite these diverse applications.

PageRank Beyond the Web

Inference and Disputed Authorship: The Federalist

Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.Different automatic learning algorithms for text categori-zation have different classification accuracy.Very accurate text classifiers can be learned automatically from training examples.

Inductive learning algorithms and representations for text categorization

https://escholarship.org/content/qt9pc2t96v/qt9pc2t96v.pdf?t=n68eqb

Introduction—Topic models: What they are and why they matter

Increasingly, management researchers are using topic modeling, a new method borrowed from computer science, to reveal phenomenon-based constructs and grounded conceptual relationships in textual da...

/pdf/topic-modeling-in-management-research-rendering-new-theory-32xxxvydh7.pdf

Topic Modeling in Management Research: Rendering New Theory from Textual Data

In this volume, Matthew L. Jockers introduces readers to large-scale literary computing and the revolutionary potential of macroanalysis--a new approach to the study of the literary record designed for probing the digital-textual world as it exists today, in digital form and in large quantities. Using computational analysis to retrieve key words, phrases, and linguistic patterns across thousands of texts in digital libraries, researchers can draw conclusions based on quantifiable evidence regarding how literary trends are employed over time, across periods, within regions, or within demographic groups, as well as how cultural, historical, and societal linkages may bind individual authors, texts, and genres into an aggregate literary culture. Moving beyond the limitations of literary interpretation based on the "close-reading" of individual works, Jockers describes how this new method of studying large collections of digital material can help us to better understand and contextualize the individual works within those collections.

Macroanalysis: Digital Methods and Literary History

We compare and benchmark the performance of five classification methods, four of which are taken from the machine learning literature, in a classic authorship attribution problem involving the Federalist Papers. Cross-validation results are reported for each method, and each method is further employed in classifying the disputed papers and the few papers that are generally understood to be coauthored. These tests are performed using two separate feature sets: a ‘‘raw’’ feature set containing all words and word bigrams that are common to all of the authors, and a second ‘‘pre-processed’’ feature set derived by reducing the raw feature set to include only words meeting a minimum relative frequency threshold. Each of the methods tested performed well, but nearest shrunken centroids and regularized discriminant analysis had the best overall performances with 0/70 cross-validation errors. .................................................................................................................................................................................

A comparative study of machine learning methods for authorship attribution

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1105&context=englishfacpubs

Significant themes in 19th-century literature

R Basics.- First Foray into Text Analysis with R.- Accessing and Comparing Word Frequency Data.- Token Distribution Analysis.- Correlation.- Measures of Lexical Variety.- Hapax Richness.- Do It KWIC.- Do It KWIC (Better).- Text Quality, Text Variety, and Parsing XML.- Clustering.- Classification.- Topic Modeling.- Appendix A: Variable Scope Example.- Appendix B: The LDA Buffet.- Appendix C: Code Repository.- Appendix D: R Resources.- Practice Exercise Solutions.- Index.

Text Analysis with R for Students of Literature

Mormon prophet Joseph Smith (1805-44) claimed that more than two-dozen ancient individuals (Nephi, Mormon, Alma, etc.) living from around 2200 BC to 421 AD authored the Book of Mormon (1830), and that he translated their inscriptions into English. Later researchers who analyzed selections from the Book of Mormon concluded that differences between selections supported Smith's claim of multiple authorship and ancient origins. We offer a new approach that employs two classification techniques: 'delta' commonly used to determine probable authorship and 'nearest shrunken centroid' (NSC), a more generally applicable classifier. We use both methods to determine, on a chapter-by-chapter basis, the probability that each of seven potential authors wrote or contributed to the Book of Mormon. Five of the seven have known or alleged connections to the Book of Mormon, two do not, and were added as controls based on their thematic, linguistic, and historical similarity to the Book of Mormon. Our results indicate that likely nineteenth century contributors were Solomon Spalding, a writer of historical fantasies; Sidney Rigdon, an eloquent but perhaps unstable preacher; and Oliver Cowdery, a schoolteacher with editing experience. Our findings support the hypothesis that Rigdon was the main architect of the Book of Mormon and are consistent with historical evidence suggesting that he fabricated the book by adding theology to the unpublished writings of Spalding (then deceased).

Matthew L. Jockers

Papers

Macroanalysis: Digital Methods and Literary History

A comparative study of machine learning methods for authorship attribution

Significant themes in 19th-century literature

Text Analysis with R for Students of Literature

Reassessing authorship of the Book of Mormon using delta and nearest shrunken centroid classification