In political science, research using computer-assisted text analysis techniques has exploded in the last fifteen years. This scholarship spans work studying political ideology,1 congressional speech,2 representational style,3 American foreign policy,4 climate change attitudes,5 media,6 Islamic clerics,7 and treaty making,8 to name but a few. As these examples illustrate, computer-assisted text analysis—a prime example of mixed-methods research—allows gaining new insights from long-familiar political texts, like parliamentary debates, and altogether enables the analysis to new forms of political communication, such as those happening on social media. While the new methods greatly facilitate the analysis of many aspects of texts and hence allow for content analysis on an unprecedented scale, they also challenge traditional approaches to research transparency and replication.9 Specific challenges range from new forms of data pre-processing and cleaning, to terms of service for websites, which may explicitly prohibit the redistribution of their content. The Statement on Data Access and Research Transparency10 provides only very general guidance regarding the kind of transparency positivist empirical researchers should provide. In this paper, we consider the application of these general guidelines to the specific context of computer-assisted text analysis to suggest what transparency demands of scholars using such methods. We explore the implications of computer-assisted text analysis for data transparency by tracking the three main stages of a research project involving text as data: (1) acquisition, where the researcher decides what her corpus of texts will consist of; (2) analysis, to obtain inferences about the research question of interest using the texts; and (3) ex post access, where the researcher provides the data and/or other information to allow the verification of her results. To be transparent, we must document and account for decisions made at each stage in the research project. Transparency not only plays an essential role in replication11 but it also helps to communicate the essential procedures of new methods to the broader research community. Thus transparency also plays a didactic role and makes results more interpretable. Many transparency issues are not unique to text analysis. There are aspects of acquisition (e.g., random selection), analysis (e.g., outlining model assumptions), and access (e.g., providing replication code) that are important regardless of what is being studied and the method used to study it. These general issues, as well as a discussion of issues specific to traditional qualitative textual analysis, are outside of our purview. Instead, we focus here on those issues that are uniquely important for transparency in the context of computer-assisted text analysis.