Human coding and computational text analysis are more powerful when combined in an iterative workflow. Text analysis tools can strategically select texts for human coders—texts representing larger samples and outlier texts of high inferential value. Preprocessing can speed up hand-coding by extracting features like names and key sentences. Humans and computers can iteratively tag entities using regex tables and group texts by key features (e.g., identify lobbying coalitions by common policy demands) Applying simple search and text-reuse methods to public comments on all U.S. federal agency rules, a sample of 10,894 hand-coded comments yields 41 million as-good-as-hand-coded comments regarding both the organizations that mobilized them and the extent to which policy changed in the direction they sought.
Iterative Human Coding and Computational Text Analysis: Assessing the Effects of Public Pressure on Policy
