Mailing list
- Text as Data mailing list sign-upSign-up for emails about future meetings and other discussions related to text as data at UMN.
Contact
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Spring 2022 workshops & events
- DASH Software CarpentryComputational workshops on Python, R, Git, the Unix shell, SQL, and OpenRefine are currently planned in March and April, 2022.
- Introduction to Python for Social Sciences (Feb 11, 2022)This LATIS/Libraries workshop will teach you how to get started using Python and some of its basic syntax, grammar and structures. It will also introduce the popular package Pandas which provides a familiar dataframe structure to import, format, and clean data as well as functions to manipulate, filter, and analyze data.
- Introduction to Computational Text Analysis (Feb 25, 2022)This hands-on LATIS/Libraries workshop will introduce some common methods such as topic modeling and sentiment analysis, as well as fundamental cleaning and processing tasks for a text analysis workflow in Python.
- Text mining library guideFind UMN library and open access resources that are available in some form for text mining (computational text analysis) purposes.
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
Previous meetings
Fall 2021 Meetings
- ProQuest Text & Data Mining Studio: Research talk + demoWed, Oct 13, 2021 | 1-2pm
Join us to learn more about ProQuest's Text & Data Mining (TDM) Studio, which provides computational access to news and scholarly articles.
* C. Daniel Myers (Political Science, UMN) will present his research on Measuring Newspaper Coverage of Congress.
* Michael Beckstrand (LATIS, UMN) will demonstrate how TDM Studio was used to create and query a unique research news corpus to support Myers' research. - CADRE: Research talk + demoWed, Nov 3, 2021 | 1-2pm
Join us to learn more about CADRE: the Collaborative Archive & Data Research Environment, which serves as a standardized text- and data-mining service for large datasets including the Web of Science citations.
* Michael Park (CSOM, UMN) will discuss Analyzing the Dynamics of Science Using the CADRE Platform, from a 2019/20 CADRE Fellow research project.
* Cody Hennesy (Libraries, UMN) will introduce how to access and use the CADRE platform for citation research.
Spring 2021 Meetings
- Intro to Computational Text Analysis in Python (March 5, 2021)This hands-on LATIS workshop will introduce some common methods such as sentiment analysis, as well as fundamental cleaning and processing tasks for a text analysis workflow in Python. Register here.
- Python Text Analytics Course (JSTOR/Constellate) - March 2021As a part of a partnership with JSTOR and their new text mining platform, Constellate, UMN students, staff and faculty have access to a free 8-session online introduction to text analytics in Python this March, 2021. The first four sessions are intended for folks new to Python. Those with Python experience can sign-up for the last four sessions on their own, where you will learn more about text mining access to JSTOR (scholarly books and articles) and Chronicling America (historical newspapers) via Constellate.
- Text as Data Practice Group: Intro to Constellate (Friday, May 7, 9:30 - 11am)Join us for an informal online workshop that will include a brief introduction to the Constellate text mining platform, followed by co-working time for folks to work through online tutorials covering Python text analysis tools and methods such word frequencies, sentiment analysis, topic modeling, TF-IDF, and NLTK. A great time to drop in with your own questions about text analytics! All levels welcome.
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!