Text is arguably the most pervasive—and certainly the most persistent—artifact of political behavior. Extensive collections of texts with clearly recognizable political—as distinct from religious—content go back as far as 2500 BCE in the case of Mesopotamia and 1300 BCE for China, and 2400-year-old political discussions dating back to the likes of Plato, Aristotle, and Thucydides are common fare even in the introductory study of political thought. Political tracts were among the earliest productions following the introduction of low-cost printing in Europe—fueling more than a few revolutions and social upheavals—and continuous printed records of legislative debates, such as the British parliament's Hansard and precursors tracing to 1802, cover centuries of political discussion.

Wordscores is a widely used procedure for inferring policy positions, or scores, for new documents on the basis of scores for words derived from documents with known scores. It is computationally straightforward, requires no distributional assumptions, but has unresolved practical and theoretical problems. In applications, estimated document scores are on the wrong scale and the theoretical development does not specify a statistical model, so it is unclear what assumptions the method makes about political text and how to tell whether they fit particular text analysis applications. The first part of the paper demonstrates that badly scaled document score estimates reflect deeper problems with the method. The second part shows how to understand Wordscores as an approximation to correspondence analysis which itself approximates a statistical ideal point model for words. Problems with the method are identified with the conditions under which these layers of approximation fail to ensure consistent and unbiased estimation of the parameters of the ideal point model.

Entries in the burgeoning “text-as-data” movement are often accompanied by lists or visualizations of how word (or other lexical feature) usage differs across some pair or set of documents. These are intended either to establish some target semantic concept (like the content of partisan frames) to estimate word-specific measures that feed forward into another analysis (like locating parties in ideological space) or both. We discuss a variety of techniques for selecting words that capture partisan, or other, differences in political speech and for evaluating the relative importance of those words. We introduce and emphasize several new approaches based on Bayesian shrinkage and regularization. We illustrate the relative utility of these approaches with analyses of partisan, gender, and distributive speech in the U.S. Senate.

In monetary policy, decision makers seek to influence the expectations of agents in ways that can avoid making abrupt, dramatic, and unexpected decisions. Yet in October 1979, Chairman Paul Volcker led the Federal Reserve's Federal Open Market Committee (FOMC) unanimously to shift its course in managing U.S. monetary policy, which in turn eventually brought the era of high inflation to an end. Although some analysts argue that “the presence and influence of one individual”—namely, Volcker—is sufficient to explain the policy shift, this overlooks an important feature of monetary policymaking. FOMC chairmen—however, omnipotent they may appear—do not act alone. They require the agreement of other committee members, and in the 1979 revolution, the decision was unanimous. How, then, did Chairman Volcker manage to bring a previously divided committee to a consensus in October 1979, and moreover, how did he retain the support of the committee throughout the following year in the face of mounting political and economic pressure against the Fed? We use automated content analysis to examine the discourse of the FOMC (with this discourse recorded in the verbatim transcripts of meetings). In applying this methodology, we assess the force of the arguments used by Chairman Volcker and find that deliberation in the FOMC did indeed “matter” both in 1979 and 1980. Specifically, Volcker led his colleagues in coming to understand and apply the idea of credible commitment in U.S. monetary policymaking.

Analysis of political communication is an important aspect of political research. Thematic content analysis has yielded considerable success both with manual and automatic coding, but Semantic Network Analysis has proven more difficult, both for humans and for the computer. This article presents a system for an automated Semantic Network Analysis of Dutch texts. The system automatically extracts relations between political actors based on the output of syntactic analysis of Dutch newspaper articles. Specifically, the system uses pattern matching to find source constructions and determine the semantic agent and patient of relations, and name matching and anaphora resolution to identify political actors. The performance of the system is judged by comparing the extracted relations to manual codings of the same material. Results on the level of measurement indicate acceptable performance. We also estimate performance at the levels of analysis by using a case study of media authority, resulting in good correlations between the theoretical variables derived from the automatic and manual analysis. Finally, we test a number of substantive hypotheses with regression models using the automatic and manual output, resulting in highly similar models in each case. This suggests that our method has sufficient performance to be used to answer relevant political questions in a valid way.

This article presents a novel automatic method of text analysis aimed at discovering patterns of lexical cohesion in political speech. The unit of analysis are groups of words with related meanings; the software is based on the results of a multiperson annotation experiment that captures reliably identified connections between words in a text. We illustrate the advantages of such a representation by juxtaposing results of a detailed hand-made analysis of Margaret Thatcher's rhetoric with analysis based on the automatically detected groups of words. We both corroborate previous findings regarding Thatcher's rhetorical tools and illuminate additional elements thereof. We suggest that lexical cohesion analysis is a promising technique to bridge the gap between quantitative and qualitative analyses of text as political material, by establishing units that are both robust enough to enable comprehensive coverage and coherent enough to support direct interpretation.

This article describes a new machine-coded event data set specifically designed to study the spatially, temporally, and tactically disaggregated actions of multiple state and nonstate actors in a systematic fashion. The project develops an extensive set of dictionaries for multiple actors and employs a new coding scheme to organize information on such actors and their behavior. The author describes the machine content-analysis methods used to collect the data and the newly developed coding scheme.