Social scientists interested in mixed-methods research have traditionally turned to human annotators to classify the documents or events used in their analyses. The rapid growth of digitized government documents in recent years presents new opportunities for research but also new challenges. With more and more data coming online, relying on human annotators becomes prohibitively expensive for many tasks. For researchers interested in saving time and money while maintaining confidence in their results, we show how a particular supervised learning system can provide estimates of the class of each document (or event). This system maintains high classification accuracy and provides accurate estimates of document proportions, while achieving reliability levels associated with human efforts. We estimate that it lowers the costs of classifying large numbers of complex documents by 80% or more.

No abstract

No abstract