Validating Text-as-Data Approaches: A Framework for NLP Applications in Political Science

The integration of Natural Language Processing (NLP) and Large Language Models (LLMs) into political science offers transformative opportunities for analyzing political texts, scaling qualitative analysis, and uncovering latent ideological patterns. However, ensuring these approaches meet the methodological standards of the discipline requires rigorous validation. Among others, validation serves to establish the accuracy of measurements, the robustness of models, the reliability of classifications, and to mitigate ethical concerns. This article proposes a comprehensive framework for validating text as data applications in political science. We proceed by offering a literature review detailing the range of text as data approaches in political science and what validation techniques authors use. After establishing that a comprehensive validation framework has yet to emerge, we propose a five-fold approach. Human evaluation: uses human coders to verify automated outputs for a subset of the data. Out-of-sample testing: Split data into training and testing sets to evaluate generalization. Replicability: Test models across multiple datasets to ensure consistent performance. Comparison with Benchmarks: Compare results to established benchmarks or baselines. Error Analysis: Identify common failure cases, test for language and political bias in the results and refine models iteratively. Overall, this framework not only addresses methodological gaps but also provides a roadmap for improving the transparency and credibility of text-as-data research in political science.