Collaborating with the Machines: A Hybrid Method for Classifying Policy Documents
提出一种结合人工编码与朴素贝叶斯自动分类的混合方法，帮助政策研究者用有限预算高效处理海量政府文本，并用政策议程经典案例验证效果。
Governments produce vast and growing quantities of freely available text: laws, rules, budgets, press releases, and so forth. This information flood is facilitating important, growing research programs in policy and public administration. However, tightening research budgets and the information's vast scale forces political science and public policy to aspire to do more with less. Meeting this challenge means applied researchers must innovate. This article makes two contributions for practical text coding—the process of sorting government text into researcher‐defined coding schemes. First, we propose a method of combining human coding with automated computer classification for large data sets. Second, we present a well‐known algorithm for automated text classification, the Naïve Bayes classifier, and provide software for working with it. We argue and provide evidence that this method can help applied researchers using human coders to get more from their research budgets, and we demonstrate the method using classical examples from the study of policy agendas.