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Automated Text Classification of News Articles: A Practical Guide

Published online by Cambridge University Press:  09 June 2020

Pablo Barberá Open the ORCID record for Pablo Barberá [Opens in a new window] ,
Amber E. Boydstun Open the ORCID record for Amber E. Boydstun [Opens in a new window] ,
Suzanna Linn Open the ORCID record for Suzanna Linn [Opens in a new window] ,
Ryan McMahon  and
Jonathan Nagler Open the ORCID record for Jonathan Nagler [Opens in a new window]
Pablo Barberá*
Affiliation:
Associate Professor of Political Science and International Relations, University of Southern California, Los Angeles, CA90089, USA. Email: [email protected]
Amber E. Boydstun
Affiliation:
Associate Professor of Political Science, University of California, Davis, CA95616, USA. Email: [email protected]
Suzanna Linn
Affiliation:
Liberal Arts Professor of Political Science, Department of Political Science, Penn State University, University Park, PA16802, USA. Email: [email protected]
Ryan McMahon
Affiliation:
PhD Graduate, Department of Political Science, Penn State University, University Park, PA16802, USA (now at Google). Email: [email protected]
Jonathan Nagler
Affiliation:
Professor of Politics and co-Director of the Center for Social Media and Politics, New York University, New York, NY10012, USA. Email: [email protected]
*
*Email: [email protected]
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Abstract

Automated text analysis methods have made possible the classification of large corpora of text by measures such as topic and tone. Here, we provide a guide to help researchers navigate the consequential decisions they need to make before any measure can be produced from the text. We consider, both theoretically and empirically, the effects of such choices using as a running example efforts to measure the tone of New York Times coverage of the economy. We show that two reasonable approaches to corpus selection yield radically different corpora and we advocate for the use of keyword searches rather than predefined subject categories provided by news archives. We demonstrate the benefits of coding using article segments instead of sentences as units of analysis. We show that, given a fixed number of codings, it is better to increase the number of unique documents coded rather than the number of coders for each document. Finally, we find that supervised machine learning algorithms outperform dictionaries on a number of criteria. Overall, we intend this guide to serve as a reminder to analysts that thoughtfulness and human validation are key to text-as-data methods, particularly in an age when it is all too easy to computationally classify texts without attending to the methodological choices therein.

Keywords

statistical analysis of textsautomated content analysiscontent analysis
Type
Articles
Information
Political Analysis , Volume 29 , Issue 1 , January 2021 , pp. 19 - 42
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Contributing Editor: Jeff Gill

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