Text-Based Ideal Points (2020)

Abstract: Ideal point models analyze lawmakers’ votes to quantify their political positions, or ideal points. But votes are not the only way to express a political position. Lawmakers also give speeches, release press statements, and post tweets. In this paper, we introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors. We demonstrate the TBIP with two types of politicized text data: U.S. Senate speeches and senator tweets. Though the model does not analyze their votes or political affiliations, the TBIP separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. One benefit of analyzing texts, as opposed to votes, is that the TBIP can estimate ideal points of anyone who authors political texts, including non-voting actors. To this end, we use it to study tweets from the 2020 Democratic presidential candidates. Using only the texts of their tweets, it identifies them along an interpretable progressive-to-moderate spectrum.

## introduction ideal point models are widely used to help characterize modern democracies, analyzing lawmakers' votes to estimate their positions on a political spectrum @xcite . but votes aren't the only way that lawmakers express political preferences-press releases, tweets, and speeches all help convey their positions. like votes, these signals are recorded and easily collected. this paper develops the text-based ideal point model (tbip), a probabilistic topic model for analyzing unstructured political texts to quantify the political preferences of their authors. while classical ideal point models analyze how different people vote on a shared set of bills, the tbip analyzes how different authors write about a shared set of latent topics. the tbip is inspired by the idea of political framing: the specific words and phrases used when discussing a topic can convey political messages @xcite . given a corpus of political texts, the tbip estimates the latent topics under discussion, the latent political positions of the authors of texts, and how per-topic word choice changes as a function of the political position of the author. a key feature of the tbip is that it is unsupervised. it can be applied to any political text, regardless of whether the authors belong to known political parties. it can also be used to analyze non-voting actors, such as political candidates. figure 1 shows a tbip analysis of the speeches of the 114th u.s. senate. the model lays the senators out on the real line and accurately separates them by party. (it does not use party labels in its analysis.) based only on speeches, it has found an interpretable spectrum-senator bernie sanders is liberal, senator mitch mcconnell is conservative, and senator susan collins is moderate. for comparison, figure 2 also shows ideal points estimated from the voting record of the same senators; their language and their votes are closely correlated. the tbip also finds latent topics, each one a vocabulary-length vector of intensities, that describe the issues discussed in the speeches. for each topic, the tbip involves both a neutral vector of intensities and a vector of ideological adjustments that describe how the intensities change as a function of the political position of the author. illustrated in the tbip is a bag-of-words model that combines ideas from ideal point models and poisson factor-figure 1 . the text-based ideal point model (tbip) separates senators by political party using only speeches. the algorithm does not have access to party information, but senators are coded by their political party for clarity (democrats in blue circles, republicans in red x's). the speeches are from the 114th u.s. senate. we develop the details of the tbip and its variational inference algorithm. we study its performance on three sessions of u.s. senate speeches, and we compare the tbip to other methods for scaling political texts @xcite . the tbip performs best, recovering ideal points closest to the vote-based ideal points. we also study its performance on tweets by u.s. senators, again finding that it closely recovers their vote-based ideal points. (in both speeches and tweets, the differences from vote-based ideal points are also qualitatively interesting.) finally, we study the tbip on tweets by the 2020 democratic candidates for president, for which there are no votes for comparison. it lays out the candidates along an interpretable progressiveto-moderate spectrum. ## the text-based ideal point model we develop the text-based ideal point model (tbip), a probabilistic model that infers political ideology from political texts. we first review bayesian ideal points and poisson factorization topic models, two probabilistic models on which the tbip is built. ## related work most ideal point models focus on legislative rollcall votes. these are typically latent-space factor models @xcite @xcite , which relate closely to item-response models @xcite . researchers have also developed bayesian analogues @xcite and extensions to time series, particularly for analyzing the supreme court @xcite . some recent models combine text with votes or party information to estimate ideal points of legislators. @xcite analyze votes and the text of bills to learn ideological language. gerrish and blei ( 2012 use votes or party information, it is applicable to all political texts, even when votes and party labels are not present. moreover, party labels can be restrictive because they force hard membership in one of two groups (in american politics). the tbip can infer how topics change smoothly across the political spectrum, rather than simply learning topics for each political party. annotated text data has also been used to pre-dict ideological positions. wordscores @xcite uses texts that are hand-labeled by political position to measure the conveyed positions of unlabeled texts; it has been used to measure the political landscape of ireland @xcite . @xcite analyze hand-labeled editorials to estimate ideal points for newspapers. the ideological topics learned by the tbip are also related to political frames @xcite . historically, these frames have either been hand-labeled by annotators @xcite or used annotated data for supervised prediction @xcite . in contrast to these methods, the tbip is completely unsupervised. it learns ideological topics that do not need to conform to pre-defined frames. moreover, it does not depend on the subjectivity of coders. ## inference the tbip involves several types of latent variables: neutral topics ˇk, ideological topics á k , topic intensities â d , and ideal points x s . conditional on the text, we perform inference of the latent variables through the posterior distribution p.â; ˇ; á; xjy/. but calculating this distribution is intractable. we rely on approximate inference. we use mean-field variational inference to fit an approximate posterior distribution @xcite @xcite . variational inference frames the inference problem as an optimization problem. set q .â; ˇ; á; x/ to be a variational family of approximate posterior distributions, indexed by variational parameters . variational inference aims to find the setting of that minimizes the kl divergence between q and the posterior. minimizing this kl divergence is equivalent to maximizing the evidence lower bound (elbo), the elbo sums the expectation of the log joint (here broken up into the log prior and log likelihood) and the entropy of the variational distribution. to approximate the tbip posterior we set the variational family to be the mean-field family. the mean-field family factorizes over the latent variables, where d indexes documents, k indexes topics, and s indexes authors: we use lognormal factors for the positive variables and gaussian factors for the real variables, we use stochastic gradient ascent. we form noisy gradients with monte carlo and the "reparameterization trick" @xcite , as well as with data subsampling @xcite . to set the step size, we use adam @xcite . we initialize the neutral topics and topic intensities with a pre-trained model. specifically, we pre-train a poisson factorization topic model using the algorithm in @xcite . the tbip algorithm uses the resulting factorization to initialize the variational parameters for â d and ˇk. the full procedure is described in appendix a. for the corpus of senate speeches described in section 2, training takes 5 hours on a single nvidia titan v gpu. we have released open source software for tensorflow and pytorch. 1 ## empirical studies we study the text-based ideal point model (tbip) on several datasets of political texts. we first use the tbip to analyze speeches and tweets (separately) from u.s. senators. for both types of texts, the tbip ideal points, which are estimated from text, are close to the classical ideal points, which are estimated from votes. we also compare the tbip to existing methods for scaling political texts @xcite . the tbip performs better, finding ideal points closer to the vote-based ideal points. finally, we use the tbip to analyze a group that does not vote: 2020 democratic presidential candidates. using only tweets, it estimates ideal points for the candidates on an interpretable progressive-to-moderate spectrum.