This guide is to assist you in the Digital Humanities-oriented work you will be doing in Dr. Gibson's AAS 400 class this semester. Patrick Williams, Librarian for Literature, Rhetoric, and Digital Humanities, led a workshop on these resources in the library, and you are welcomed to reach out to him with questions and concerns: 315.443.9520 / email@example.com / 551 Bird
Voyant Tools is a browser-based suite of text analysis tools that quickly and easily can get you reading and visualizing your texts in a number of different ways. You paste in text or upload a file or multiple files for comparison.
Google Fusion Tables
Fusion Tables is an experimental data visualization web application to gather, visualize, and share data tables. You need a google account to use this tool, and it works with more complex tabular data for large-scale comparisons.
Tableau is another advanced tool that has applications with all kinds of datasets. It has featured widely in data journalism projects, but people have used Tableau to analyze all sorts of things, including film scripts, information about birds, and airline flight data. You can explore some methods for using Tableau with Corey Jones's blog post "How and why I learned Tableau as a student."
One thing you will need to begin using these tools is some text. For music lyrics, you can use a site like Genius / Rap Genius or Lyrics AZ to create a corpus of texts to analyze. All you need to do is paste lyrics into a text document to get started. You may want to create a corpus of a single song, all of the tracks on an album, or even all the albums from a particular artist. You'll want to save these in a .txt file--using notepad or another simple text editor is best for simple textual data.
You can also create a corpus from texts you view online: tweets based on a particular user account or hashtag, books from the Internet Archive, screenplays for films found online, or anything else of interest.
From your syllabus:
For this assignment, students will research 4 songs from 2 albums of the same artist and create an original dataset and 2 data visualizations.
As you will see once you begin working, data will have some problems you can address to make sure that you're seeing what you intend to see. You may need to edit your cut-and-paste document to remove the kinds of text you're not interested in analyzing... this might be names of speakers, page numbers, or any other paratextual stuff that has made its way into your data. As any seasoned DH practitioner will tell you, cleaning your data is often where you put the most effort, so think abuot what needs to be in each of these files as you prepare them.
African-American Studies Scholars have been leaders in Digital Humanities projects making use of all sorts of textual, mapping, and visualization technologies.
These projects create and distribute materials relating to African American history and experience across many institutions, and many of these projects make access to collections and to research methods possible online that were not possible before. Among them: