Political Text Scaling Meets Computational Semantics
This work addresses a key bottleneck for political scientists by improving the accuracy of text scaling through semantic analysis, though it is incremental as it builds on existing computational linguistics and clustering techniques.
The authors tackled the problem of text scaling in political science by developing SemScale, a semantically aware algorithm that outperforms traditional frequency-based methods in capturing political dimensions from European Parliament speeches across five languages and two legislative terms.
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.