Collaborating with the Machines: A Hybrid Method for Classifying Policy Documents. Issue 1 (29th January 2018) - Loftis, Matt W.; Mortensen, Peter B.
Abstract : 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. 摘要: 政府向公众提供着数量庞大且不断增加的可自由获取的文本, 如法律、规则、预算、新闻稿等。受这一信息泛滥的情况的影响, 政策和公共管理领域的科研项目在数量和重要性上正在增长。然而, 日益收紧的研究预算和庞大的信息规模迫使政治学学科"少费多用"。迎接这一挑战意味着应用学科的研究人员必须进行创新。本文为应用文本编码（将政府文本通过研究人员定义的编码进行分类转译的过程）做出了两个贡献 。首先, 我们提出了一种方法, 可以将大规模数据集的人工编码与计算机自动分类相结合 。其次, 我们介绍了一种众所周知的自动文本分类算法, 即朴素贝叶斯分类器, 并且提供了与之协同工作的软件。 我们认为, 这种方法可以帮助应用学科的研究人员在有限的研究经费下利用人类编码器获得更多的研究成果；并且, 我们通过政策议程研究中的经典实例证明了这一观点。