Image of Using Supervised Machine Learning to Code Policy Issues Can Classifiers Generalize across Contexts?

Journal Articles

Using Supervised Machine Learning to Code Policy Issues Can Classifiers Generalize across Contexts?

Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles and parliamentary questions. Comparing computer-based annotations with human annotations shows that our method approaches the performance of human coders. Furthermore, we investigate the capability of an automatic coding tool, which is based on supervised machine learning, to generalize across contexts. We conclude by highlighting implications for methodological advances and empirical theory testing.

Availability

No copy data

Detail Information
Series Title
THE ANNALS OF THE AMERICAN ACADEMY OF POLITICAL AND SOCIAL SCIENCE
Call Number
-
Publisher
Thousand Oaks : Sage Publications.,
Collation
-
Language
English
ISBN/ISSN
978-1-5063-1463-1
Classification
NONE
Content Type
-
Media Type
-
Carrier Type
-
Edition
Volume 659, May 2015, pages 122-133
Subject(s)
Agenda setting
Content Analysis
Big data
Machine learning
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

File Attachment
No Data
Comments
You must be logged in to post a comment