This post continues a dialogue between Mark Ravina and TJ Greer on text mining and the recent student protest movement. In this entry we examine the potential for sentiment analysis. Mark: I’m somewhat cynical about sentiment analysis.
This is the first of a series of blog post applying text mining and other DH techniques to the evolving student protest movement demanding racial equality. What can DH techniques tell use about student demands and administration responses? Are faculty and students talking to each other or at each other? This project is a collaborative effort between Mark Ravina, a professor of history at Emory University, and TJ Greer, a soon-to-be Emory College graduate and history major.
In a previous post I suggested that historians should use quantitative methods less to answer existing questions than to pose new ones. Such a digital humanities (DH) approach would be the reverse of the older social science history approach, in which social science tools were use to “answer” definitively longstanding questions.
What’s most striking about Leon Wieseltier’s essay in the New York Times Book review is how it confirms almost every cliché about the humanities as technophobic, insular, and reactionary. Not to mention some stereotypes about grouchy old men. Now I should confess at the outset to being a longtime Wieseltier cynic. His misreadings of popular culture always seemed mildly ridiculous. But what’s striking about the NYT piece is his vast ignorance of the subject.
In fall 2014 I taught a freshman seminar on data visualization entitled “Charts, Maps, and Graphs.” Over the course of the semester I worked with the students to create vizs that passed Tukey’s “intra-ocular trauma” test: the results should hit you between the eyes. Over the coming months I’ll be blogging based on their final projects. Today’s post is based on the work of Jeffrey You, who used US professional sports data, comparing baseball and football.