Suresh Naidu


Text-Based Ideal Points
Keyon Vafa | Suresh Naidu | David Blei
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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.


Detecting Latent Ideology in Expert Text: Evidence From Academic Papers in Economics
Zubin Jelveh | Bruce Kogut | Suresh Naidu
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


Historical Analysis of Legal Opinions with a Sparse Mixed-Effects Latent Variable Model
William Yang Wang | Elijah Mayfield | Suresh Naidu | Jeremiah Dittmar
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)