Collaborating with the Machines: A hybrid method for classifying policy documents

Matthew Loftis, Peter Bjerre Mortensen

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Burgeoning government activity has enabled an important and growing research program into the content and determinants of policy agendas around the world. However, tightening research budgets and the vast scale of available information force political science to aspire to do more with less. Meeting this challenge requires innovation in managing and preparing data. This paper makes two contributions to the practice of data coding to measure the content of political agendas. First, we propose a method of combining human content coding of political agendas and automated computer classification to classify large data sets. Second, we present software and supporting tools to apply a well-known algorithm for automated text classification, the Naı̈ve Bayes classifier. We demonstrate its usefulness for coding large sets of highly unbalanced multiclass data of the sort used to study the political agenda and demonstrate how our hybrid approach can maximize the returns on research budgets.
Original languageEnglish
JournalPolicy Studies Journal
Pages (from-to)184-206
Number of pages23
Publication statusPublished - 2020


  • Classifying policy documents
  • Machine coding
  • Policy agendas


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