Text-Based Ideal Points

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 (), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors. We demonstrate the 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 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 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-tomoderate spectrum.

Align Voting Behavior with Public Statements for Legislator Representation Learning

“…Since votes are not the only way to express political preferences, other sources of data including speech and knowledge graph (Budhwar et al, 2018;Gentzkow et al, 2019;Patil et al, 2019;Vafa et al, 2020) have been applied to estimate ideology. Although previous studies (Bruns and Highfield, 2013;Golbeck and Hansen, 2014;Barberá, 2015;Peng et al, 2016;Wong et al, 2016;Boutyline and Willer, 2017;Johnson et al, 2017) have incorporated social network of following or retweeting on Twitter to learn legislators, fine-grained attitudes of legislators remain unknown since the texts themselves have not been mined.…”

“…Although previous studies (Bruns and Highfield, 2013;Golbeck and Hansen, 2014;Barberá, 2015;Peng et al, 2016;Wong et al, 2016;Boutyline and Willer, 2017;Johnson et al, 2017) have incorporated social network of following or retweeting on Twitter to learn legislators, fine-grained attitudes of legislators remain unknown since the texts themselves have not been mined. Until recently, Preoţiuc-Pietro et al (2017) started to analyze linguistic differences between ideologically different groups using a broad range of handcrafted language features, and studies (Vafa et al, 2020;Spell et al, 2020) explored to incorporate Twitter texts to cap-ture nuances in legislators' preferences via statistical methods. In spite of this, there has been little research attempting to combine votes with public statements to portray legislators from both angles and predict their behavior.…”

“…Secondly, it is unable to model the behavior of newly-elected legislators because their historical voting records are not available (i.e., coldstart problem). Meanwhile, researchers explore to use public statements to characterize the ideology of legislators with the guidance of framing theory (Entman, 1993;Chong and Druckman, 2007;Baumer et al, 2015;Vafa et al, 2020). Vafa et al (2020) propose a text-based ideal point model to analyze tweets of legislators independent of roll call data.…”

“…Meanwhile, researchers explore to use public statements to characterize the ideology of legislators with the guidance of framing theory (Entman, 1993;Chong and Druckman, 2007;Baumer et al, 2015;Vafa et al, 2020). Vafa et al (2020) propose a text-based ideal point model to analyze tweets of legislators independent of roll call data. Experiment results show some correlations between distributions of ideal points learned from legislative data and public statements.…”

Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews

“…Our proposed BTM is partly inspired by the recently developed Text-Based Ideal Point (TBIP) model (Vafa et al, 2020) in which the topic-specific word choices are influenced by the ideal points of authors in political debates. However, TBIP is fully unsupervised and when used in customer reviews, it generates topics with mixed polarities.…”