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Webinar Details

An Intro to Text Analysis for Social Scientists

Patrick van Kessel
Thursday, December 12, 2019
12:00 Noon Central Time

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About This Course:

From open-ended survey questions to social media streams, text data can offer social scientists rich insights that extend beyond the traditional survey – but it can also pose substantial challenges, often including the need for extensive human coding. Fortunately, modern computational tools are making it easier to analyze free-form text. This webinar will introduce the basics of working with text data quantitatively, provide an overview of common text analysis methods and tools, and demonstrate some of them in action.  

Learning objectives:
  • The meaning of terms like “TF-IDF,” “regular expressions,” and “support vector machines”
  • The fundamentals of processing and converting text into quantitative data
  • The concepts behind common methods for analyzing text data
  • Where to find free tools to get started with text analysis
Webinar Level:
About the Instructors:

VanKessel_Patrick-Edited.jpgPatrick van Kessel is a senior data scientist at Pew Research Center, specializing in computational social science research and methodology. He is the author of studies that have used natural language processing and machine learning to measure negative political discourse and news sharing behavior by members of Congress on social media, and is involved in the ongoing development of best practices for the application of data science methods across the Center. Van Kessel received his master’s degree in social science from the University of Chicago, where he focused on open-ended survey research and text analytics. He holds bachelor’s degrees in economics and political science from the University of Texas at Austin. Prior to joining Pew Research Center, he worked at NORC at the University of Chicago as a data scientist and technical advisor on a variety of research projects related to health, criminal justice and education.