# Text-Based Ideal Points

arXiv: https://arxiv.org/abs/2005.04232 · HTML: https://mlsanity.com/paper/2005.04232/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 22 of 22)

- [Natural Language Decompositions of Implicit Content Enable Better Text Representations](https://mlsanity.com/paper/2305.14583/) (2023)
- [Measurement in the Age of LLMs: An Application to Ideological Scaling](https://mlsanity.com/paper/2312.09203/) (2023)
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- [What Is The Political Content in LLMs' Pre- and Post-Training Data?](https://mlsanity.com/paper/2509.22367/) (2025)
- [An Item Response Theory Framework for Persuasion](https://mlsanity.com/paper/2204.11337/) (2022)
- [Disentangling Active and Passive Cosponsorship in the U.S. Congress](https://mlsanity.com/paper/2205.09674/) (2022)
- [Adaptive political surveys and GPT-4: Tackling the cold start problem with simulated user interactions](https://mlsanity.com/paper/2503.09311/) (2025)
- [Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers](https://mlsanity.com/paper/2310.12575/) (2023)
- [A Structural Text-Based Scaling Model for Analyzing Political Discourse](https://mlsanity.com/paper/2410.11897/) (2024)
- [Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews](https://mlsanity.com/paper/2101.10150/) (2021)
- [Evolving Voices Based on Temporal Poisson Factorisation](https://mlsanity.com/paper/2410.18486/) (2024)
- [Multi-environment Topic Models](https://mlsanity.com/paper/2410.24126/) (2024)
- [Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms](https://mlsanity.com/paper/2511.13238/) (2025)
- [Embeddings for Preferences, Not Semantics](https://mlsanity.com/paper/2605.08360/) (2026)
- [Tracking Brand-Associated Polarity-Bearing Topics in User Reviews](https://mlsanity.com/paper/2301.07183/) (2023)
- [It's All Relative: Interpretable Models for Scoring Bias in Documents](https://mlsanity.com/paper/2307.08139/) (2023)
- [CommunityFish: A Poisson-based Document Scaling With Hierarchical Clustering](https://mlsanity.com/paper/2308.14873/) (2023)
- [Seeded Poisson Factorization: leveraging domain knowledge to fit topic models](https://mlsanity.com/paper/2503.02741/) (2025)
- [Framework of Voting Prediction of Parliament Members](https://mlsanity.com/paper/2505.12535/) (2025)
- [Objectifying the Subjective: Cognitive Biases in Topic Interpretations](https://mlsanity.com/paper/2507.19117/) (2025)
- [Graph-Augmented LLMs for Swiss MP Ideology Prediction](https://mlsanity.com/paper/2605.04643/) (2026)
- [Making Political Text Scaling Comparable: Infrastructure and Hyperparameter Sensitivity for 17 Algorithms](https://mlsanity.com/paper/2609.17602/) (2026)

## Abstract (arXiv)

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.