Text as Data Practice Group

Spring 2022 workshops & events

Text as Data Practice Group

The Text as Data Practice Group offers periodic gatherings focused on specific computational tools, methods and projects that we will learn and discuss together. The group is discipline-agnostic, and all levels are welcome to experiment and learn from the tutorials and talks at hand, and from each other. The group operates in the spirit of creating community, nurturing peer-to-peer learning, and exploring emerging text and data analysis methods and tools on campus.  

Open to students, staff, faculty, and all curious parties. Bring a laptop!

Facilitated by: Michael Beckstrand (LATIS), Cody Hennesy (Libraries), and Wanda Marsolek (Libraries).

Online courses

Below are some options available for self-paced online learning in areas related to text mining topics.

Constellate (JSTOR)

Online lessons in scholarly text analysis methods (e.g. word frequencies, TF/IDF) using JSTOR data, as well as beginner's lessons for Jupyter notebooks and Python.

LinkedIn Learning

Login to LinkedIn Learning to start 

Previous meetings

Fall 2021 Meetings

Spring 2021 Meetings

Spring 2020 Meetings

Text as Data Lightning Talks (Round Two)
Wed, May 13, 3-4pm  (Online)

The final meeting of our inaugural semester will feature another round of lightning talks from UMN researchers using "text as data" methods in their own scholarship. 

  • Mariya Gyendina (UMN TC, Libraries) - Consulting based on who I think you are: Mixed methods study of feedback in an online writing center
  • Siyu Li (UMN TC, Political Science) - Evolving Norm of Collegiality: Analyzing the Sentiments of Supreme Court Oral Arguments
  • Ted Pedersen (UMN Duluth, Computer Science) - Automatic Detection of Hate Speech and Islamophobia

Text as data lightning talks
Wed, April 15, 3-4pm (Online)

Join us for a series of short lightning talks from staff and graduate students about their own text as data research. From issues in data sourcing and cleaning, to working with methods and interpreting results, this will be an opportunity to discuss possible approaches and solutions with peers across the disciplines.

  • Kelsey Neis (Office of Info Technology) - Text as data as text adventure
  • Neeraj Rajasekar (Sociology) - Diversity discourse in the news: A Quantitative content analysis of "diversity" in political news media
  • Cody Hennesy & David Naughton (Libraries) - Computational analysis of Library Quarterly

Sentiment Analysis for Exploratory Data Analysis
Wed, Feb 19, 2020, 3-4pm (Wilson Library Collaboration Studio)
Join us for our first meeting, where we’ll work through the Programming Historian’s tutorial on Sentiment Analysis in Python. Sentiment Analysis is a form of natural language processing that seeks to quantify the emotional intensity of words and phrases within a text or texts. This tool can be helpful for analyzing interview transcripts, newspaper articles on a specific topic, or even poetry! 

Installation and laptop set-up: if you can already use Python on your laptop, you should be all set. If you’re new to Python, we recommend installing Anaconda for Python 3.7. If you need any installation help, you can email Cody (chennesy@umn.edu) or stop by on Feb 19 at 2:30 for hands-on help!

Last Updated: Feb 10, 2022 1:38 PM