# Political Text Scaling Meets Computational Semantics

arXiv: https://arxiv.org/abs/1904.06217 · HTML: https://mlsanity.com/paper/1904.06217/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [Optimizing text representations to capture (dis)similarity between political parties](https://mlsanity.com/paper/2210.11989/) (2022)
- [Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers](https://mlsanity.com/paper/2310.12575/) (2023)
- [Natural Language Processing for Policymaking](https://mlsanity.com/paper/2302.03490/) (2023)
- [L(u)PIN: LLM-based Political Ideology Nowcasting](https://mlsanity.com/paper/2405.07320/) (2024)
- [Toeing the Party Line: Election Manifestos as a Key to Understand Political Discourse on Twitter](https://mlsanity.com/paper/2410.15743/) (2024)
- [Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms](https://mlsanity.com/paper/2511.13238/) (2025)
- [CommunityFish: A Poisson-based Document Scaling With Hierarchical Clustering](https://mlsanity.com/paper/2308.14873/) (2023)
- [Strategies for political-statement segmentation and labelling in unstructured text](https://mlsanity.com/paper/2503.07179/) (2025)
- [Comparing Architectures for Supervised Political Scaling](https://mlsanity.com/paper/2607.01464/) (2026)
- [Making Political Text Scaling Comparable: Infrastructure and Hyperparameter Sensitivity for 17 Algorithms](https://mlsanity.com/paper/2609.17602/) (2026)

## Abstract (arXiv)

During the last fifteen years, automatic text scaling has become one of the key tools of the Text as Data community in political science. Prominent text scaling algorithms, however, rely on the assumption that latent positions can be captured just by leveraging the information about word frequencies in documents under study. We challenge this traditional view and present a new, semantically aware text scaling algorithm, SemScale, which combines recent developments in the area of computational linguistics with unsupervised graph-based clustering. We conduct an extensive quantitative analysis over a collection of speeches from the European Parliament in five different languages and from two different legislative terms, and show that a scaling approach relying on semantic document representations is often better at capturing known underlying political dimensions than the established frequency-based (i.e., symbolic) scaling method. We further validate our findings through a series of experiments focused on text preprocessing and feature selection, document representation, scaling of party manifestos, and a supervised extension of our algorithm. To catalyze further research on this new branch of text scaling methods, we release a Python implementation of SemScale with all included data sets and evaluation procedures.